# sMRI-PatchNet: A novel explainable patch-based deep learning network for
Alzheimer’s disease diagnosis and discriminative atrophy localisation with
Structural MRI

Xin Zhang  Liangxiu Han  Lianghao Han  Haoming Chen  Darren Dancey  Daoqiang
Zhang Xin Zhang, Liangxiu Han, Darren Dancey are with the Department of
Computing, and Mathematics, Manchester Metropolitan University, Manchester
M15GD, U.K (e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>d.dancey@mmu.ac.uk)Lianghao Han is with the Department of Computer Science,
Brunel University, Uxbridge UB8 3PH, U.K
(e-mail:lianghao.han@brunel.ac.uk)Haoming Chen is with the Department of
Computer Science, University of Sheffield, Sheffield S1 4DP U.K
(e-mail:hchen78@sheffield.ac.uk)Daoqing Zhang is with College of Computer
Science and Technology, Nanjing University of Aeronautics and Astronautics,
P.R.China (e-mail:dqzhang@nuaa.edu.cn)Corresponding author*: L. Han (e-mail:
<EMAIL_ADDRESS>
###### Abstract

Structural magnetic resonance imaging (sMRI) can identify subtle brain changes
due to its high contrast for soft tissues and high spatial resolution. It has
been widely used in diagnosing neurological brain diseases, such as
Alzheimer’s disease (AD). However, the size of 3D high-resolution data poses a
significant challenge for data analysis and processing. Since only a few areas
of the brain show structural changes highly associated with AD, the patch-
based methods dividing the whole image data into several small regular patches
have shown promising for more efficient sMRI-based image analysis. The major
challenges of the patch-based methods on sMRI include identifying the
discriminative patches, combining features from the discrete discriminative
patches, and designing appropriate classifiers. This work proposes a novel
patch-based deep learning network (sMRI-PatchNet) with explainable patch
localisation and selection for AD diagnosis using sMRI. Specifically, it
consists of two primary components: 1) A fast and efficient explainable patch
selection mechanism for determining the most discriminative patches based on
computing the SHapley Additive exPlanations (SHAP) contribution to a transfer
learning model for AD diagnosis on massive medical data; and 2) A novel patch-
based network for extracting deep features and AD classfication from the
selected patches with position embeddings to retain position information,
capable of capturing the global and local information of inter- and intra-
patches. This method has been applied for the AD classification and the
prediction of the transitional state moderate cognitive impairment (MCI)
conversion with real datasets. The experimental evaluation shows that the
proposed method can identify discriminative pathological locations effectively
with a significant reduction on patch numbers used, providing better
performance in terms of accuracy, computing performance, and generalizability,
in contrast to the state-of-the-art methods.

Deep learning, Feature extraction, Alzheimer’s disease, Brain modeling,
Structural MRI

##  1 Introduction

Alzheimer’s disease (AD) is a degenerative brain illness and the most
prevalent cause of dementia, accounting for 60% to 80% of cases [1].
Currently, there is no cure for AD. However, an accurate and timely AD
diagnosis can give patients the best chance to prepare a treatment plan that
may change the disease progression and reduce the symptom[2]. So far, brain
atrophy[3], gray matter atrophy [4], and regional atrophy [5], are considered
as the most critical neurodegeneration bio-markers. Structural magnetic
resonance imaging (sMRI) as a non-invasive method measures brain morphometry,
and is able to capture the subtle brain changes induced by the atrophic
process, thanks to its high contrast for soft tissues and high spatial
resolution [6]. It has been used in detecting AD and moderate cognitive
impairment (MCI) with various computer vision methods[6, 7, 8]. The existing
sMRI-based AD diagnostic methods usually partition the entire MR image into
multiple regions for better feature extraction of local abnormal brain
structural changes [9, 10, 11, 12, 13]. Depending on the partition scale, it
can be broadly grouped into three categories: 1) Voxel-based; 2) Regions of
interest (ROIs)-based and 3) Patch-based methods.

The voxel-based sMRI diagnostic methods take whole images as input and extract
global voxel-wise features for AD diagnosis[14, 15, 16]. Features such as the
probability maps of gray matter (GM) [14], white matter (WM) [17], and
cerebrospinal fluid (CSF) [18] are widely used. However, there exist some
limitations [19] including 1) Due to the high dimensionality of the feature
extracted from the data, the number of data used for model training is
relatively small, resulting in computationally intensive and over-fitting. 2)
Ignoring area information of brain that has been shown to be important in the
diagnosis of AD.

To alleviate the aforementioned problems, several existing works focused on
some predetermined ROIs guided by prior biological knowledge and extracted
regional features for AD diagnosis [20, 21, 22, 23, 24, 25]. However since
these methods are based on empirical regions, they might neglect possible
pathological locations in the whole brain [26]. The features extracted from
the ROI may not capture the microscopic variation that is involved in the
brain [27]. Additionally, segmenting ROIs based on expert knowledge is
resource intensive.

To locate the subtle brain atrophy regions for the early diagnosis of AD and
avoid fine-grained segmentation, patch-based methods, as a compromise between
voxel-based and ROI-based methods, are proposed for the effective capture of
small local structural changes in sMRI images. Unlike the other two types of
methods, this type of methods segments an sMRI image into multiple small
fixed-size regular 3D patches based on prior-knowledge/pre-defined anatomical
landmarks [28, 26, 29] or statistics methods [30]. However, the pre-defined
patch selection approach may miss some AD-related atrophy patches. Statistic
analysis such as the T-test does not necessarily identify the correct regions
linked with AD due to carry-over effects and lacks explainability [31]. In
these approaches, the feature extraction and classification from the selected
patches are often based on conventional machine learning methods such as
Support Vector Machines (SVM) [32], and Linear programming boosting [33]. In
[34, 35, 36], the authors used the handcrafted features, which may degrade the
classification performance due to the heterogeneity between features and
subsequent classification algorithms. Recently, deep learning methods based on
convolutional neural networks (CNNs) for AD diagnosis directly learn feature
representations from input patches without needing feature selection [26, 37,
29, 30, 38], making the whole process much more convenient and less prone to
error and bias. However, in these methods, an image is firstly partitioned
into patches and then each selected input patch is fed into the CNN
independently. The local position information of each patch and the spatial
relationship between patches are not included in the CNN computation. Few deep
learning methods with the black-box nature of neural networks have specific
output functions for pathological location positioning. As cerebral atrophy
typically appears to be localised, only a few areas on sMRI scanning have
significant structural changes highly correlated with the pathological
characteristics, while the remaining areas give few useful distinguishing
information. Hence, two remaining challenges in the patch-based methods are 1)
how to accurately locate and select the patches; 2) how to capture both local
and global features for improved explainable AD diagnosis. To address these
challenges, this work proposes a novel patch-based neural network (sMRI-
PatchNet) with explainable patch localisation and selection for Alzheimer’s
disease diagnosis and discriminative atrophy using Structural MRI. Our
contributions include:

  1. 1.

An explainable patch localisation and selection for discriminative atrophy
regions is proposed, in which the fewest number of AD-related patches with
explainability are selected based on a novel fast recursive partition
perturbation method for computing the SHapley Additive exPlanations (SHAP)
contribution to a transfer learning model for AD diagnosis on massive medical
data. This significantly reduces computational complexity and enhances
explainability.

  2. 2.

A novel patch-based deep learning model (sMRI-PatchNet) is proposed for
improving AD diagnosis performance, in which a learned position embedding is
added to the patch presentation to retain the position and spatial
relationship of patches. The sMRI-PatchNet has three main parts: Global
spatial information (GSI) and Local patch information (LPI), are used to
capture global feature between patches and local features within a patch
efficiently, and a classifier for feature classification.

  3. 3.

The proposed approach has been evaluated against real datasets with the
corresponding visualization. From a clinical perspective, the visualization
results of brain regions covered by selected patches show that the proposed
method can effectively identify discriminative pathological locations. These
new biomarkers can help clinicians in clinical diagnosis.

The remaining part of this paper is organized as follows: Section 2 presents
the related work; Section 3 details the proposed method; Section 4 and 5
describe the experimental evaluation and results. Section 6 provides the
discussion on the potential of clinical translation and the limitations of the
proposed work; Section 7 concludes the work.

##  2 Related work

In this section, we review patch-based brain diagnosis methods using sMRI and
explainable artificial intelligence methods.

###  2.1 Patch-based brain diagnosis methods in sMRI

Based on partitioning scales, the existing sMRI-based AD diagnostic methods
can be broadly divided into three categories: 1) voxel-based, 2) regions of
interest (ROIs) based, and 3) patch-based methods. The voxel-based methods are
intuitive and straightforward in terms of the interpretation of results,
aiming to identify disease-related microstructures from sMRIs of patients. The
key point of this type of methods is to find suitable image features to
estimate the probability of different tissue classes in a given voxel, such as
gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) [39].
However, only analysing the features of isolated voxels would lead to the
ignorance of high correlations between voxels. Another limitation of voxel-
level methods is the overfitting problem because the voxel-level feature
representations always have higher dimensionality compared with the number of
image samples in model training. Several feature dimensionality reduction
algorithms are used to solve this issue, e.g. a sparse coding method with a
hierarchical tree-guided regularisation [40]. An alternative solution to
feature extraction is to use 3D CNN. In [41], the authors have demonstrated
that using 3D CNNs to extract features for AD classification can achieve
better accuracy than traditional hand-crafted feature extraction approaches.
In [13], the authors have designed a self-attention 3D CNN to improve the
diagnosis performance by adding attention to global features. However, the
main limitation of 3D CNN methods is their extra-high computation costs caused
by 3D convolution operations. In contrast, ROI-based approaches are based on
the predefined regions identified from prior biological knowledge, such as the
shrinkage of cerebral cortices, hippocampi and ventricles etc. [42, 22, 24].
These methods require a much lower feature dimensionality than the whole
voxel-based methods. However, disease-related structural/functional changes
occur in multiple brain regions. The ROI-based approach may neglect disease-
related features or fail to capture small and subtle changes associated with
brain diseases [27]. Additionally, segmenting ROIs based on experts knowledge
is resource intensive, which remains a challenging task [29].

To address these limitations, the patch-based methods have been proposed, in
which brain regions are split into several small fix-sized 3D patches. Regular
patches eliminate the need for region segmentation in the dataset, and each
patch is a region of interest. Since brain atrophy usually occurs locally,
only a few of the regions in sMRI scans have noticeable structural changes,
highly associated with pathological features. The existing works have been
mainly focusing on two main challenges :1) how to select patches and combine
the local patches to capture global information of the whole brain sMRI? 2)
how to extract representative features and classify the patches into the right
categories?

For the first challenge, empirical knowledge-based and statistical analysis-
based methods have been used for the patch selection. On one hand, the
empirical knowledge-based methods select the patches in the important regions
based on prior knowledge. For example, Lian et al [26] adopted anatomical
landmarks defined in the whole brain image as prior knowledge for generating
selected patches. These anatomic landmarks were defined using a shape
constraint regression forest model [27]. On the other hand, the statistical
analysis-based selection methods use statistical algorithms to calculate the
patch differences between Alzheimer’s disease (AD) and Normal cohort (NC)
patients. The patches with the highest variance are selected as the
discriminative patches. In previous studies [40, 43, 29, 30], a T-test was
used to find the difference between AD patients and NC group data for each
patch. The patches with p-values smaller than 0.05 were selected. In the study
[44], the authors used the weighted correlation coefficient [45] as the
similarity measure to select discriminative patches. However, the statistical
significance for voxels in each patch does not necessarily have a link with
AD. Therefore, the explainable patch selection is still a challenging task.

For the second challenge, research efforts have been made on the feature
extraction and classification of patched data. Liu et al. [43] first developed
a patch-based AD diagnosis method with an independent feature extraction for
each patch. The features were then integrated hierarchically at the classifier
level. Inspired by Liu’s method, Suk et al. [29] proposed a systematic method
for a joint feature representation from the paired patches of sMRI images
using a patch-based approach. Tong et al. [46] developed a multiple instance
learning (MIL) model for AD classification and MCI conversion prediction using
local intensity patches as features. Zhu et al. [30] proposed a dual attention
multi-instance deep learning network (DA-MIDL) for the early diagnosis of AD,
in which a Patch-Nets with spatial attention blocks was used for extracting
discriminative features of each patch. It has been proven that these patch-
based methods can efficiently deal with the problem of high dimensional
features and sensitivity to slight brain structure changes.

However, in the patch-based approaches described above, each selected patch is
fed into the CNN independently. The local position information of each patch
and the spatial relationship between patches are not included in the CNN
computation. Few deep learning methods with the black-box nature of neural
networks have specific output functions for pathological location positioning.
Therefore, accurately identifying the discriminative patches while capturing
both local and global features for improved explainable AD diagnosis is still
a remaining challenge in patch-level methods.

###  2.2 Explainable methods

Recently, machine learning (ML) methods, including deep learning (DL), have
been enormously successful in various fields [47]. However, they are still
seen as a “black box” model due to their multilayer nonlinear structure. These
models have been criticized for lack of transparency, and their predicted
results are not traceable [48]. Interpreting and explaining a “black box”
model is extremely important in real applications. A reasonable interpretation
of an ML model can increase the user’s trust and provide helpful information
to improve the model. So far, there have been many general interpretation
methods for ML/DL models, and have given birth to a new subfield, explainable
artificial intelligence (XAI) [49]. Based on the algorithmic approaches used,
the XAI methods in medical image analysis for visual explanation can be
categorized into two types: backpropagation-based and perturbation-based
methods [50]. The backpropagation-based methods focus on the back-propagation
of gradients through the neural network to highlight pixel attributions [51,
52]. The saliency map is the first interpretation method that generates a
visual explanation using the back-propagation on the convolutional network
[52]. The guided back-propagation method is another gradient-based XAI method
to improve the saliency map by restricting the back-propagation of values less
than 0 [53]. Class Activation Mapping (CAM) is also a widely used XAI method.
The CAM replaces the last fully connected layers with convolutional layers to
keep the object positions. This operation help discover the spatial
distribution of discriminative regions for the predicted category [54]. In the
paper [13], the authors used the CAM method to explain the deep learning
model’s decision on AD diagnosis. However, the backpropagation-based methods
are criticized for being inherently dependent on the model and data-generating
process [55]. Ghorbani et al. [56] and Kindermans et al.[57] have shown that
small perturbations or simple transformations to the input generated much more
significant changes in the interpretations than the backpropagation-based
methods did. The perturbation-based XAI methods focus on perturbing the input
to assess the attribution of pixels in certain areas. The feature set of the
input is perturbed through occlusion, removing, masking, conditional sampling,
and other techniques. Then, the forward pass of the perturbed input is used to
generate the attribution representations without the need for backpropagating
gradients [58, 59]. The Local Interpretable Model-Agnostic Explanations (LIME)
is one of the most widely used perturbation-based XAI methods because it can
explain any classifier in an interpretable and faithful manner [59]. To
generate a representation that explains the model’s decision, LIME tries to
find the importance of contiguous super-pixels in an input image towards the
output class. Shapley additive explanations (SHAP) is a similar method that
uses the classical Shapley values from game theory to show the importance to
the models [60, 61]. However, the perturbation-based XAI methods have the
challenge of combinatorial complexity explosion. This happens when one
attempts to go through all elements of the input and all their possible
combinations to observe how each of them would affect the output [62]. The
possible combinations of data perturbations increase dramatically when dealing
with 3D images, causing a significant increase in computational costs. To
avoid the combinatorial explosion, a fast perturbation method is proposed in
this paper.

##  3 The Proposed Method

This study aims to propose a novel patch-based convolutional network (sMRI-
PatchNet) with an explainable patch selection for AD diagnosis with sMRI
images. It involves two-level classifications: Alzheimer’s disease (AD) vs.
Normal cohort (NC), and progressive MCI (pMCI) vs. Stable MCI (sMCI).

The schematic diagram of our framework is shown in Fig. 1, which consists of
two major units: Explainable Patch Localisation and Selection (EPLS) for patch
selection and sMRI-PatchNet for feature extraction and classification. The
rationale behind this architecture includes:

  1. 1.

Unlike traditional statistical or prior knowledge-based methods, we have
proposed an explainable patch localisation and selection method. It can
accurately identify the fewest number of AD-related patches based on a novel
fast recursive partition perturbation method for computing the SHapley
Additive exPlanations (SHAP) contribution to a transfer learning model for AD
diagnosis on massive medical data. This significantly reduces computational
complexity and enhances explainability.

  2. 2.

A novel patch-based deep learning network (sMRI-PatchNet) is proposed for
feature extraction and classification. The selected patches are flattened into
vectors using a linear projection. A learned position embedding is added to
the patch presentation to retain their location and spatial information. Two
CNN blocks including global spatial information (GSI) and local patch
information (LPI) are proposed to capture the global features between patches
and local features within a patch. A classifier consisting of average pooling
followed by a fully connected layer is connected to predict output classes.

Figure 1: The flowchart of the proposed method

###  3.1 Explainable Patch Localisation and Selection (EPLS)

The unit of explainable patch localisation and selection (EPLS) aims to
identify and select the most discriminative patches by evaluating their
importance to the AD diagnosis (classification). It was implemented through
transfer learning using MedicalNet [63] pretrained on 23 publicly available
large medical image data, and fine-tuned with an sMRI dataset. The
classification accuracy of MedicalNet can reach 0.909. As shown in Fig. 2, in
this unit, each sMRI image is uniformly partitioned into 3D cubic patches with
a fixed size, without overlapping. Based on our experiments and the previous
work [26], the size of $25\times 25\times 25$ is selected in this study. These
patches are fed into the MedicalNet for evaluating their importance to AD
diagnosis. A fast explainable recursive partition perturbation approach for
assessing the patch importance based on the value of the SHAP coefficient has
been designed. To explain the model’s decision, we perturb the sMRI data by
filling value of 0 on specific patch and observing how the model output
changes to the perturbations. Then, the SHAP coefficient is calculated to
measure the contribution of each location of the sMRI input to the model
output. This is aligned with human intuition and can effectually discriminate
among model output classes [61]. We average the contribution of each location
for all sMRI images identified as AD, and the high contribution locations are
selected as input to the AD diagnosis model.

Figure 2: The flowchart of the fast recursive partition XAI method

####  3.1.1 Fast recursive partition perturbation method

In this work, each sMRI image is divided into n 3D cubic patches (n=598 when
the patch size is $25\times 25\times 25$ on a standard sMRI image). The
computational complexity of the model explanation on each image will be
$2^{n}$ by using the conventional permutation method to perturb the data. This
will take an unacceptable amount of time to work through all the sMRI data.

To reduce the computational complexity and costs without compromising the
performance, this work introduces a fast recursive partition perturbation
method to perturb the image hierarchically. Unlike the conventional
permutation methods which ignore the link between patches, our proposed method
calculates the importance of each patch by iteratively partitioning the data.
The algorithm is shown in Fig. 3. Specifically, to avoid predefining regions
of interest, we partition an image, $X$, into disjointed regular patches
$\left(X_{1,0},X_{1,1},X_{1,2}\ldots X_{1,n}\right)$, called level 1, the size
of each patch is 100 × 100 × 100, and n is the number of patches (n = 8 is
chosen at Level 1 for 3D images). After computing the SHAP coefficients of all
patches at Level 1, $\left(S_{1,0},S_{1,1},S_{1,2}\ldots S_{1,n}\right)$, we
further partition each patch into 8 smaller sub-patches in a hierarchical
manner up to Level 3, based on its SHAP value. If the calculated SHAP
coefficient for a patch is greater than a pre-defined threshold ($\tau$), then
there is no further partition to the next level; or else, if $S_{1,i}<\tau$,
we partition this patch to the next level; and recursive this manner up to
Level 3. The patch sizes are $50\times 50\times 50$ at Level 2 and $25\times
25\times 25$ at Level 3. After the recursion, all the patches along with their
SHAP coefficient values are returned.

This partitioning method matches the data structure of the image and
significantly reduces the computational complexity.

Figure 3: 3.1.1. Fast recursive partition perturbation method

####  3.1.2 SHAP coefficient estimation method

The SHAP coefficient [60] is used to calculate the contribution of each patch
towards the output of AD classification) and can be defined as:

$S_{i}(f)=\sum_{C\subseteq[n]\backslash\\{i\\}}\frac{|C|!(n-|C|-1)!}{n!}\left[f\left(X_{C\cup\\{i\\}}\right)-f\left(X_{C}\right)\right]$
(1)

Where C is a subset of n patches used in the model, X is the vector of all
patch features and n is the number of patches in the input.
$f\left(X_{C\cup\\{i\\}}\right)$ is the predicted probability of AD with the
$ith$ patch included while $f\left(X_{C}\right)$ is the probability value
without the $ith$ patch in the input. $S_{i}(f)$ represents the averaged
marginalized contribution of ith patch over all possible subsets of n. The
computation complexity is exponential in the dimension of the input features
($O(2^{n})$).

###  3.2 sMRI-PatchNet

Figure 4: The architecture of the sMRI-PatchNet Model

The second major unit is a patch-based deep learning convolutional network for
feature extraction and classification, named sMRI-PatchNet, as shown in Fig.
4. After the patch selection, these selected patches are flattened into
vectors ($\mathrm{x}_{p}\in\mathrm{R}^{M\times\left(P^{3}\right)}$) and mapped
to d dimensions (the size of vectors) using a linear projection. Where P is
the size of the patch, and M is the number of selected patches. To retain the
positional information, a learned 1D position embedding [64] is added to the
patch vectors.

$\mathrm{X}=\left[\mathrm{x}_{\mathrm{p}}^{1}\mathrm{E};\mathrm{x}_{\mathrm{p}}\mathrm{E};\cdots;\mathrm{x}_{\mathrm{M}}^{\mathrm{M}}\mathrm{E}\right]+\mathrm{E}_{\mathrm{pos}},\\\
\mathrm{E}\in\mathrm{R}^{\left(\mathrm{P}^{3}\right)\times\mathrm{d}},\mathrm{E}_{\mathrm{pos}}\in\mathrm{R}^{\left(\mathrm{P}^{3}\right)\times\mathrm{d}}$
(2)

We then group patch vectors in sequence to a new array
($\mathrm{X}\in\mathrm{R}^{(d+1)\times(\mathrm{m}\times\mathrm{m})}$), where
$m\times m=M$ is the number of patches, d is the size of the array’s Z-axis
which denotes the dimension of each flattened patch. The Z-axis (d) represents
the information inside each patch. The xy-axis ($m\times m$) represents the
spatial information between the different small patches. The sMRI-PatchNet
consists of global spatial information (GSI), local patch information (LPI),
and a classifier. GSI is used to capture global information between the
patches from the XY-axis and the LPI is used to capture the local features
within a patch from Z-axis (d). The classifier consists of average pooling and
a fully connected layer that classifies features into the correct class.

####  3.2.1 Global spatial information (GSI)

The GSI is proposed to capture the global spatial information of patches. In
the first part, the 3D patches are flattened into vectors and then converted
to a 2D array
($\mathrm{X}\in\mathrm{R}^{d\times(\mathrm{m}\times\mathrm{m})}$), which can
reduce the computational consumption caused by 3D convolution. Then a spatial-
wise 2D convolution with a large kernel size of $m\times m$ is used to operate
on $\mathrm{X}\in\mathrm{R}^{d\times(\mathrm{m}\times\mathrm{m})}$ to extract
the global spatial information. The kernel size $m\times m$ allows the
receptive field of convolution to cover the entire area. Then, the
conventional activation ($\sigma$, Rectified Linear Unit(ReLU) [65] ) and
BatchNorm (BN [66]) are used following the GSI module to accelerate the model
training. A residual connection [67] is introduced before and after the GSI to
avoid the gradient vanishing problem when the depth of the model is increased.

$\left.X_{l}^{\prime}=\mathrm{BN}\left(\text{ SpatialwiseConv
}\left(X_{l-1}\right)\right)\right)+X_{l-1}$ (3)

####  3.2.2  Local patch information (LPI)

The LPI is proposed to capture the internal information of each patch. Note
that the voxel values of each patch are flattened on the Z-axis in the input
array with dimension d. Therefore, a pointwise 2D convolution with kernel size
$1\times 1$, regarded as a multilayer perceptron (MLP), is executed on the
channel axes. The activation and Batchnorm are also followed after the LPI
module.

$Z_{l+1}=\mathrm{BN}\left(\sigma\left(\text{ PointwiseConv
}\left(X_{l}^{\prime}\right)\right)\right)$ (4)

##  4 Experimental Evaluation

###  4.1 Dataset description and preprocessing

This study used the ADNI dataset, which was obtained from the public
Alzheimer’s Disease Neuroimaging Initiative (ADNI) database
(http://adni.loni.usc.edu). In the ADNI dataset, a total of 1193 1.5T/3T
T1-weighted structural magnetic resonance imaging (sMRI) scans are taken from
the baseline/screening visits (i.e. first examination) of subjects in the
three ADNI phases (ADNI-1, ADNI-2 and ADNI-3). These participants can be
classified into three groups: AD (Alzheimer’s disease), MCI (mild cognitive
impairment) and NC (normal controls) according to standard clinical criterias.
For the prediction of MCI conversion, MCI subjects are further categorised
into two groups: pMCI (progressive MCI subjects who convert to AD within 36
months of the baseline visit) and sMCI (stable MCI subjects who are
consistently diagnosed with MCI). The study’s ADNI dataset contained 389 AD,
172 pMCI, 232 sMCI and 400 NC subjects. The demographic details of this
dataset are described in Table. 1.

Table 1: Demographic details of the studied subjects including dataset, group
type, gender, age, mini-mental state examination (mmse) and clinical dementia
rating (cdr).

Dataset | Group | Gender | Age | Edu | MMSE
---|---|---|---|---|---
(Male/Female) | (Mean±Std) | (Mean±Std) | (Mean±Std)
ADNI-1 | AD | 200 ( 103 / 97 ) | 75.62 ± 7.70 | 14.68 ± 3.20 | 23.29 ± 2.04
pMCI | 172 ( 106 / 66 ) | 76.34 ± 7.15 | 15.76 ± 2.84 | 26.61 ± 1.70
sMCI | 232 ( 154 / 78 ) | 76.47 ± 7.82 | 15.58 ± 3.17 | 27.31 ± 1.79
NC | 231 ( 119 / 112 ) | 75.99 ± 5.00 | 16.06 ± 2.84 | 29.12 ± 0.99
ADNI-2/3 | AD | 153 ( 85 / 68 ) | 74.95 ± 7.80 | 15.88 ± 2.66 | 23.03 ± 2.14
NC | 419 ( 170 / 249 ) | 74.84 ± 6.60 | 16.63 ± 2.48 | 29.09 ± 1.19

The original structural MRI data from the ADNI database are pre-processed for
subsequent feature learning and classification. As the original dataset is in
Neuroimaging Informatics Technology Initiative (NIfTI) format, the preprocess
is needed for spatial distortion correction caused by gradient nonlinearity
and B1 field inhomogeneity. This is a standard pipeline process including
anterior commissure (AC)-posterior commissure (PC) correction, intensity
correction [68], and skull stripping [69]. We have used MIPAV (Medical Image
Processing, Analysis, and Visualisation) application to implement AC-PC
correction and use FSL (FMRIB Software Library v6.0) for skull stripping. A
linear registration strategy (flirt instruction in FSL) is also executed to
align every sMRI linearly with the Colin27 template [70] to delete global
linear differences (including global translation, scale, and rotation
differences), and also to re-sample all sMRIs to have the identical spatial
resolution. After the preprocessing, all sMRI images have the same size,
containing $181\times 217\times 181$ voxels.

###  4.2 Evaluation metrics

We have evaluated two binary classification tasks: AD classification (i.e., AD
vs. NC) and MCI conversion prediction (i.e., pMCI vs. sMCI). The
classification performance is evaluated based on four commonly used standard
metrics, including classification accuracy (ACC), sensitivity (SEN),
specificity (SPE), and Area under the curve (AUC). These metrics are defined
as:

$ACC=\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{TN}+\mathrm{FP}+\mathrm{FN}}$
(5) $SEN=\frac{TP}{TP+FN}$ (6)

$SPE=\frac{TN}{TN+FP}$ (7)

Where $TP=TruePositive$, $TN=TrueNegative$, $FP=FalsePositive$ and
$FN=FalseNegative$. The AUC is calculated based on all possible SEN and 1-SPE
obtained by changing the thresholds performed on the classification scores
yielded by the trained networks.

###  4.3 Experimental evaluation

To evaluate the performance of our proposed model, we have conducted three
types of experiments: 1) Diagnostic performance evaluation. 2)
Generalisability evaluation and 3) Impact of discriminative patch location
selection on model performance.

####  4.3.1 Experiment One: Diagnostic performance evaluation

In this experiment, we evaluate the diagnostic performance of our proposed
model. The detailed configuration of the proposed PatchNet is shown in Table.
2. The patch size is $25\times 25\times 25$, and top 36 patches with the
highest SHAP value are selected. These configurations are the best
combinations obtained in our experiments. The model has 16 layers (D). The
dimension of the flattened patch is 1600.

Table 2: The model configuration

Model | sMRI-PatchNet
---|---
Patch size | 25
Patch number | 36
Heads | Na
Depth (D) | 12
Dimension | 1600
Param (M) | 34.53

The proposed model is compared with several commonly used automatic AD
diagnosis methods, including

1) Three traditional machine learning (ML) based methods representing a
typical example of the three types of existing computer-aided diagnostic
methods for AD, respectively including:

a) A voxel-based method (VBM) from Ashburner et al [71]. In VBM, each sMRI is
processed by the spatial normalization to a standard stereotactic brain space
(i.e., Colin27 template) and the local gray matter density is measured as the
voxel-level feature.

b) A region-based method (RBM) by Zhang et al, [72]. The RBM uses the prior
knowledge identified regions of the sMRI image as the input. After a
deformable registration [73], an entire brain sMRI image is segmented into 93
areas according to the template with 93 manually labeled ROIs [74], as
described in [72]. The grey matter volume in each ROI is then calculated as a
region-level feature, which is normalized by the total intracranial volume;

c) A patch-based method (PBM) from Zhang et al. [40]. The PBM uses selected
patches as the input. The patch location selection method proposed in their
study is used to evaluate the contribution of each patch to AD. The top 40
patches are selected, and a patch pool is operated on selected patches from
tissue density maps to generate input vectors. The Light Gradient Boosting
Machine (LightGBM) machine learning classifier is selected for feature
classification. It has state-of-the-art accuracy, lower memory usage, and
higher efficiency that can handle large-scale data [75]. The detailed
parameters for the LightGBM classifier are shown in Table. 3.

2) One deep learning model based on transfer learning: The MedicalNet (Med3D)
[63]. It is also the trained model that we used for the explainable
discriminative location selection. The Med3D adopts the ResNet family (ResNet
10, ResNet 18) architecture as the backbone [67]. To enable the Med3D to train
with 3D medical data, all 2D convolution kernels are replaced with their 3D
versions. To avoid overfitting when trained on the limited volume of training
data, Med3D collected the dataset from several medical challenges to build a
large dataset and provided a pre-trained model for other downstream tasks. In
this work, we use this pre-trained model and fine-tune it with sMRI data for
our AD diagnosis tasks.

3) Two typical patch-based deep learning methods, HFCN [26] and DA-MIDL [30].
Both of these methods used the statistical method for patch selection and
proposed novel CNN models for patch feature extraction and classification. The
HFCN model is implemented by multi-layer convolutional structures. It contains
three-level networks consisting of patch-level, region-level, and subject-
level sub-networks. Multi-scale feature representations are jointly learned
and fused for the construction of hierarchical classifiers. The features from
different levels are spatially combined to feed into the classifier. The DA-
MIDL model consists of three primary components: a) Patch-Nets with spatial
attention blocks for extracting features within each patch; b) an attention
multi-instance learning (MIL) pooling operation for balancing the relative
contribution of each patch, and c) an attention-aware global classifier for
further learning the features and making the AD-related classification
decisions.

Table 3: The LightGBM classifier training parameters

Parameters | Description | Value
---|---|---
Boosting_type | Method of boosting | Gradient-boosted decision trees (gbdt)
Num_leaves | Max number of leaves in one tree | 31
Max_depth | Limit the max depth Forr tree model, -1 means no limit | -1
Num_iterations | Number of boosting iterations | 100
Learning_rate | The shrinkage rate for model train | 0.05

In this test, we train our model using the ADNI-1 dataset and perform 10 times
of five-fold cross-validation. The dataset is randomly split into five groups
where four groups (80% of the dataset) are used for training, and the rest are
used for testing. The experimental results for classification performance are
the average of the accuracies and and its standard deviation on the testing
set across all folds. This allowed a more appropriate model analysis and made
it possible to avoid overfitting problems. To optimize model parameters, Adam
[76], a stochastic optimization algorithm with a batch size of 8 samples, has
been used in training the proposed network. We first set the initial learning
rate (LR) as $1\times 10^{-4}$. The LR is decreased to $1\times 10^{-6}$ with
increased iterations. CrossEntropy has been selected as the loss function for
this task [77]. The experiments are implemented based on PyTorch and executed
on a server with an Intel(R) Xeon(R) CPU E5-2650, NVIDIA 2080TI, and 64 GB
memory.

####  4.3.2 Experiment Two: Generalisability Evaluation

In this experiment, the generalisability and repeatability of the proposed
PatchNet model are evaluated. We train the model based on the whole ADNI-1
dataset and test it on two independent datasets (ADNI-2 and ADNI-3). Due to a
lack of pMCI and sMCI samples from ADNI-2 and ADNI-3, we only evaluate the
model performance on the AD vs. NC classification task. The four automatic
diagnosis methods described in the previous section are used for comparison.

####  4.3.3 Experiment Three: The impact of discriminative patch selection on
model performance

In this experiment, we evaluate the influence of patch selections on the
classification performance of our proposed classification model, based on two
different patch selection approaches, our SHAP-based method, and the
traditional statistic method. We investigate the effect of the patch number on
classification performance and the performance of two different patch
selection methods on identifying the locations of patches in the brain.
Considering that the patch choice is based on its contribution to AD, we use
the AD vs. NC classification task for evaluation. In our SHAP-based patch
selection approach, the top 16, 36, and 64 patches with the highest SHAP value
are selected, respectively. The number of selected patches has to be
squareable to match the PatchNet requirement (that is, it can be converted to
an m×m array). The traditional statistical analysis-based patch selection
method used in [40, 43, 29, 30] is selected for comparison. This method
assumes that the patch locations with the most significant differences between
the AD and NC groups are more likely to be the brain regions with abnormal
atrophy. Thus, the t-test [78] is applied to two groups of patch-level
features at one patch location from the same amount of AD patients and normal
controls in the training set, respectively. The p-value for each patch
location is used to sort the informativeness in all patches. The patches with
lower p-values are selected. Here we orderly select the number from 20, 40,
60, 120, 200 in the PatchNet and record the corresponding results. Identifying
morphological changes in the brain can help the clinical diagnosis of AD [5,
79, 80]. To investigate the performance of the two patch selection methods on
identifying the brain regions associated with AD, we quantitatively visualize
the patch locations predicted as AD and the regions where they are located in
the brain. Accurately identifying these regions can provide valuable
information for clinical diagnosis.

##  5 Results

###  5.1 Results of Diagnostic Performance Evaluation

Table 4: Results for AD classification (i.e., AD vs. NC) and MCI conversion
prediction (i.e., pMCI vs. sMCI)

Model | AD vs. NC classification | pMCI vs. sMCI classification
---|---|---
ACC | SEN | SPE | AUC | ACC | SEN | SPE | AUC
VBM [71] | 0.815±0.043 | 0.755±0.05 | 0.873±0.036 | 0.884±0.037 | 0.682±0.054 | 0.629±0.08 | 0.714±0.06 | 0.706±0.053
RBM [72] | 0.808±0.107 | 0.717±0.103 | 0.883±0.106 | 0.849±0.072 | 0.669±0.084 | 0.573±0.094 | 0.741±0.073 | 0.696±0.034
PBM [40] | 0.838±0.089 | 0.726±0.124 | 0.871±0.05 | 0.847±0.028 | 0.682±0.071 | 0.4±0.096 | 0.73±0.071 | 0.637±0.048
Med3D-18 [63] | 0.909±0.149 | 0.896±0.142 | 0.924±0.151 | 0.952±0.085 | 0.806±0.047 | 0.773±0.054 | 0.833±0.055 | 0.817±0.033
HFCN [26] | 0.882±0.047 | 0.89±0.054 | 0.883±0.055 | 0.929±0.033 | 0.807±0.046 | 0.806±0.049 | 0.798±0.036 | 0.794±0.036
DA-MIDL [30] | 0.904±0.079 | 0.887±0.092 | 0.903±0.075 | 0.922±0.034 | 0.809±0.092 | 0.771±0.101 | 0.826±0.097 | 0.851±0.047
Our Method | 0.920±0.088 | 0.920±0.119 | 0.919±0.052 | 0.967±0.023 | 0.819±0.044 | 0.818±0.055 | 0.816±0.056 | 0.857±0.029

The results of AD vs. NC classification and MCI conversion prediction achieved
by our sMRI-PatchNet model and the competing methods on the ADNI-1 dataset are
shown in Table. 4. The proposed sMRI-PatchNet method achieves the best
accuracy (0.920 and 0.819) in the two classification tasks, which are
statistically significant. Of the three machine learning-based baseline
methods, the PBM outperforms the RBM and VBM methods, indicating that patch-
level feature representations could offer better discriminative information
regarding the subtle brain changes for brain disease diagnosis.

Moreover, as shown in Table. 4, the Med3D-18, which uses the whole image as
input, surpasses the three traditional machine learning-based methods (WBM,
RBM, and PBM) with different input representations by significant margins in
both tasks. It demonstrates that with the transfer learning from the massive
medical dataset training [63], Med3D can effectively extract useful high-level
features from the entire sMRI image for the classification task. In addition,
the deep learning-based methods learn high-level features from data in an
incremental manner with a massive number of parameters and non-linear
calculations, thus allowing better performance than traditional machine
learning models.

###  5.2 Results of Generalizability Evaluation

Table 5: Results of AD classification on the independent ADNI2 and 3 datasetss

| AD vs. NC classification
---|---
Model | ACC | SEN | SPE | AUC
VBM | 0.806±0.046 | 0.578±0.049 | 0.866±0.036 | 0.816±0.036
RBM | 0.789±0.102 | 0.522±0.103 | 0.865±0.102 | 0.795±0.076
PBM | 0.825±0.088 | 0.775±0.119 | 0.866±0.052 | 0.884±0.023
Med3D-18 | 0.874±0.147 | 0.795±0.141 | 0.915±0.148 | 0.906±0.089
HFCN | 0.851±0.012 | 0.749±0.014 | 0.855±0.009 | 0.865±0.026
DA-MIDL | 0.868±0.034 | 0.772±0.092 | 0.893±0.101 | 0.901±0.097
Our method | 0.891±0.019 | 0.791±0.068 | 0.882±0.046 | 0.925±0.023

Table. 5 shows the AD classification results of our method and the competing
methods evaluated on the independent ADNI2 &3 datasets. Our proposed sMRI-
PatchNet generally outperforms the other five competing methods. the sMRI-
PatchNet obtains the highest accuracy (0.891) in the AD vs. NC classification,
outperforming VBM (0.806), RBM (0.789), PBM (0.825), Med3D-18 (0.874), HFCN
(0.851) and DA-MIDL(0.868). These results indicate that the sMRI-PatchNet can
provide robust performance across different datasets.

In general, the performance of a model is expected to decrease when evaluating
on the independent dataset. The accuracy and AUC of our proposed model
slightly decrease by 2% and 4%, respectively. These results indicate the good
generalization capability of our method for AD diagnosis. The accuracy of
machine learning-based methods, such as VBM, RBM, and PBM, only drops by
around 1%. This may be due to the fact that the ML-based methods have fewer
parameters, allowing the model to avoid overfitting.

###  5.3 The Impact of Discriminative Patch Location Selection on Model
Performance

Fig. 5 shows the distributions of discriminative patches selected by the
statistical analysis method and the proposed explainable SHAP-based method.
The discriminative patch locations determined by the SHAP-based method focus
more on the central part of the sMRI image, while the results from the
statistical analysis method are discrete and distributed in various regions.

Figure 5: The discriminative patch location distribution determined by: (a)
the proposed explainable SHAP based method and (b) the statistic analysis
method. Figure 6: Results of Accuracy and AUC in AD classification obtained
by: (a) our proposed explainable SHAP-based method and (b) the statistic
analysis method with different selected numbers of input image patches.

Fig. 6 shows the changes in the classification performance of our sMRI-
PatchNet model with the increasing number of input image patches selected by
the two methods, in terms of accuracy and AUC. It can be observed that
PatchNet achieves satisfactory accuracy and AUC using the input patches
selected by our proposed patch selection approach, even though the number of
selected patches (n) is only 16. In contrast, the classification accuracy of
PatchNet with the input patches selected by the statistic analysis method is
only 0.846, after selecting a larger number (60) of input patches. Only under
the circumstance of increasing the number of patches selected by the statistic
method from 60 to 140, both the accuracy and AUC are significantly improved.
This implies that the locations determined by the statistical methods are not
necessarily correct, and a smaller number of patches are insufficient to yield
satisfactory results. In our implementations, n = 36 is chosen to make a
compromise between the computational complexity, the memory cost of training,
and including a large enough number of potentially valuable locations.

##  6 Discussions

In this section, we first analyse the influence of the number and size of
patch selection on the performance of the proposed model and its potential for
clinical translation. Then, we compare our proposed method with previous
studies on AD-related brain disease diagnosis.

###  6.1 Influence of Number and Size of Patches

As a patch-based method, the size and number of patches are important
parameters. We discuss the influence of the number and size of patches on the
performance of the proposed model. Fig. 6 a) shows the AD classification
results achieved by the proposed model respectively with a range from 16 to
64. We can observe that both ACC and AUC are stable and better in the range of
16 to 64. The best performance is achieved when n increases to 36. This
implies that small numbers of patches (e.g., n=16) may not include adequate
patches related to AD classification. While, large numbers of patches (e.g.,
N=64) will increase the number of patches with useless information for AD
classification.

Figure 7: AD classification performance of the proposed model with the input
patches of different sizes ( $15\times 15\times 15$ to $35\times 35\times 35$)
on the ADNI test set.

In this paper, we select a patch size of $25\times 25\times 25$, the same as
used [26, 30]. We evaluate the AD classification performance with different
patch sizes. Fig. 7 shows the AD classification performance with different
patch sizes in a range of $15\times 15\times 15$ to $35\times 35\times 35$.
The result shows the proposed model achieve a stable performance for all
selected patches, and indicating that the model is not sensitive to the size
of input patches within this range. The accuracy of the proposed model is
greater than 0.9 for all selected patch sizes, except 15x15x15. This implies
that a relatively large patch size is required in order to capture sufficient
details of feature changes by brain atrophy.

###  6.2 Discriminative Pathological Locations and the Potential of Clinical
Translation

Figure 8: Discriminative AD-associated locations automatically identified by
our proposed explainable SHAP-based method and the statistic analysis method.
The first column shows the top 10 informative patch locations suggested by the
statistical analysis. The second and third columns show the informative patch
locations suggested by the proposed SHAP-based method with thresholds 0.7 and
0.5, respectively. The rightmost column shows the marked brain regions where
the suggested patches are gathered by the proposed method.

In Fig. 8, we visualize the locations of selected patches for AD diagnosis and
their corresponding brain regions in the automated anatomical atlas (AAL 3V1)
[81]. Nearly half of the suggested discriminative locations by the statistic
method are in the Posterior fossa, and the rest are in the cerebrum.

However, the discriminative locations suggested by the proposed method cover
47 of 170 brain structures. Table. 6 lists 31 brain regions to which the
patches suggested by our SHAP-based approach correspond in the brain atlas AAL
3V. They include Frontal Lobe, Temporal Lobe, Posterior Fossa, Insula and
Cingulate Gyri, Occipital Lobe, Parietal Lobe, Central Structures, etc. These
regions, such as the Precentral gyrus, Superior frontal gyrus, Middle frontal
gyrus, Inferior frontal gyrus [82], Supplementary motor area [83], Olfactory
cortex [84], Hippocampus, Parahippocampal gyrus, Amygdala [85], Insula [86],
Lingual gyrus [87], Precuneus [88], Caudate nucleus [89], etc. are reported to
be associated with AD. Specifically, the hippocampus is strongly linked to
long-term memory. The impact of AD-related brain shrinkage on the hippocampus
has been scientifically validated [24]. The amygdala is considered to
influence emotional functioning as well as learning and memory management
[90]. The thalamus is linked to cognition and information processing speed
[91], which are also relevant to AD. These pieces of evidence imply the
feasibility of our proposed method for identifying AD-associated areas and can
inform clinicians on AD diagnosis.

Table 6: A list of brain regions that the suggested patches from our method
cover in the automated anatomical atlas (AAL 3V1) [81].

Description | Code | Description | Code
---|---|---|---
Frontal Lobe | Insula and Cingulate Gyri
Precentral gyrus | 2 | Insula | 33,34
Superior frontal gyrus, dorsolateral | 4 | Middle cingulate & paracingulate gyri | 38
Middle frontal gyrus | 6 | Posterior cingulate gyrus | 39,40
Inferior frontal gyrus, triangular part | 9 | Occipital Lobe
IFG pars orbitalis | 11,12 | Calcarine fissure and surrounding cortex | 47
Supplementary motor area | 16 | Lingual gyrus | 52
Olfactory cortex | 17,18 | Parietal Lobe
Superior frontal gyrus, medial | 20 | Postcentral gyrus | 62
Gyrus rectus | 23,24 | Precuneus | 71,72
Medial orbital gyrus | 25,26 | Central Structures
Posterior orbital gyrus | 29,30 | Caudate nucleus | 75,76
Temporal Lobe | Lenticular nucleus, Putamen | 77,78
Hippocampus | 41 | Lenticular nucleus, Pallidum | 79,80
Parahippocampal gyrus | 43,44 | Pallidum (PAL) | 81,82
Amygdala | 45,46 | Ventral anterior | 126
Superior temporal gyrus | 85 | Pulvinar medial | 145,146
Temporal pole: superior temporal gyrus | 87,88 | Nucleus accumbens | 158
Posterior Fossa |  |  |
Lobule IV, V of vermis | 115 |  |

###  6.3 Comparison with Previous Works

For a broad comparison between our method and related studies on the
performance of AD diagnosis, in Table 8 we list the results of several state-
of-the-art models reported in the literature for AD classification and MCI
conversion prediction tasks using structural MRI data from the ADNI database,
including two voxel-level methods [92, 13], two ROI-level methods [93, 94] and
four patch-level methods [26, 43, 46, 30]. The following observations can be
noted in Table 8. 1) Our method has achieved a competitive performance in both
AD-related classification tasks. 2) Compared with traditional machine
learning-based methods such as SVM [92, 46], LDA [94], and KNN [93], the deep
learning-based methods have better performance, particularly for more
difficult MCI conversion tasks. The possible reason is that deep learning
methods have more parameters and can therefore deal with the spatial features
and correlation of the 3D data better than machine learning methods. Compared
to the other two deep learning patch-based methods, HFCN and DA-MIDL, the
proposed method achieves better accuracy. As we mentioned in the Introduction
section, the 3D convolution operation brings increased parameters and around
six times the computational complexity (3×3 kernel size) than the 2D
convolution operation. Table 6 shows the computational complexity and the
number of parameters of the four deep learning-based methods. The Med3D with
10 layers, HFCN, DA-MIDL, and sMRI-PatchNet have a similar number of
parameters (around 35 Million). The Med3D with 18 layers has the highest
number of parameters (63.53 Million). However, in terms of computational
complexity, the Med3D, HFCD and DA-MIDL are all use 3D convolution operation.
Their computational complexities are 169.55 GMac and 240.73 GMac and 220.63
GMac, respectively. Our proposed sMRI-PatchNet uses 2D convolution and has the
minimal computational complexity (2.21GMac). (3) Unlike ROI-based methods
relying on empirically predetermined ROIs, the proposed sMRI-PatchNet
automatically extracts important areas from multiple patches distributed in
the whole brain. This is much more difficult. However, our method still
obtains good performance, implying the effectiveness of our model for
identifying the location of pathology.

Table 7: Referential comparison on sMRI-based studies for ad classification
and mci conversion prediction

References | Feature | Method | Subject | AD vs. NC classification | pMCI vs. sMCI classification
---|---|---|---|---|---
[92] | Voxel-based | SVM | 137AD+134sMCI+76pMCI+162NC | 0.89 | 0.81 | 0.95 | 0.7 | 0.57 | 0.78
[13] | 3D-CNN | 353AD+232sMCI+172pMCI+591NC | 0.91 | 0.91 | 0.92 | 0.82 | 0.81 | 0.81
[94] | ROIs-based | LDA | 194AD+234sMCI+161pMCI+226NC | 0.87 | 0.9 | 0.92 | 0.773 | 0.69 | 0.79
[93] | KNN | 192AD+229sMCI+168pMCI+229NC | 0.89 | 0.86 | 0.9 | 0.7 | 0.68 | 0.71
[46] | Patch-based | SVM | 198AD+238sMCI+167pMCI+231NC | 0.9 | 0.86 | 0.93 | 0.72 | 0.79 | 0.74
[43] | Landmark detection + 3D CNN | 199AD+226sMCI+167pMCI+229NC | 0.91 | 0.88 | 0.94 | 0.77 | 0.42 | 0.82
[26] | Hierarchical-CNN | 358AD+465sMCI+205pMCI+429NC | 0.88 | 0.89 | 0.88 | 0.81 | 0.81 | 0.8
[30] | Attention+MIL+CNN | 398AD+232sMCI+172pMCI+400NC | 0.9 | 0.89 | 0.9 | 0.81 | 0.77 | 0.83
Proposed Method | Custom CNN | 353AD+232sMCI+172pMCI+591NC | 0.92 | 0.92 | 0.92 | 0.82 | 0.82 | 0.86

Table 8: The Computational complexity and parameters of proposed and Med3D
methods

Model name | Computational complexity (Gflops) | Number of parameters | Accuracy
---|---|---|---
Med3D -10 Layer | 169.55 GMac | 35.47 Million | 0.873
Med3D -18 Layer | 253.69 GMac | 63.53 Million | 0.909
HFCN | 240.73 GMac | 37.86 Million | 0.882
DA-MIDL | 220.63 GMac | 36.54 Million | 0.904
Our Method | 2.21 GMac | 34.53 Million | 0.920

##  7 Conclusion

This study has proposed a patch-based convolutional network with explainable
patch location suggestions for Alzheimer’s Disease Diagnosis. First, we
propose a fast and efficient explainable method for patch location suggestions
through computing the SHapley Additive exPlanations (SHAP) contribution to a
transfer learning model for AD diagnosis on massive medical data. A fast
recursive partition perturbation method is introduced to effectively perturb
the data to provide a fast estimation for the SHAP value of each patch. It has
significantly reduced the number of patches required for achieving a good
classification performance with 36 patches only, in contrast to 140 patches
used in the existing statistical-based methods. Consequently, it dramatically
reduces the computational complexity of the model, enabling efficient 3D data
processing and analysis. Then, a novel patch-based convolutional network
(sMRI-PatchNet) is designed to extract deep features of the discriminative
patches and applied to AD classification and its transitional state moderate
cognitive impairment (MCI) conversion prediction. The visualization results of
brain regions covered by selected patches show that the proposed method can
effectively identify discriminative pathological locations. These new
biomarkers can help clinicians in clinical diagnosis. The classification
performance and generalisability of our proposed method have been evaluated on
two independent datasets and also compared with the five state-of-the-arts
methods. The results show that the proposed model outperforms the existing
methods and has good generalizability in all cases. Moreover, it dramatically
reduces the computational complexity and computational costs, compared to
traditional deep learning methods. Future research will apply this method to
more medical data and related disease diagnoses.

## Acknowledgment

This work is supported by the Royal Society - Academy of Medical Sciences
Newton Advanced Fellowship (NAF$\backslash$R1$\backslash$180371). L.H. Han was
funded by the UK Engineering and Physical Science Research Council
(EP/W007762/1) and the Small Business Research Initiative (Innovate UK, SBRI
Funding Competitions: Heart Failure, Multi-morbidity and Hip Fracture).

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