# Generative Adversarial Networks for Data Augmentation

Angona Biswas
Research and Development Department, Pioneer Alpha,
Dhaka, Bangladesh
<EMAIL_ADDRESS>
&MD Abdullah Al Nasim
Research and Development Department, Pioneer Alpha,
Dhaka, Bangladesh
<EMAIL_ADDRESS>
&Al Imran
Research and Development Department, Pioneer Alpha,
Dhaka, Bangladesh
<EMAIL_ADDRESS>
&Anika Tabassum Sejuty
Research and Development Department, Pioneer Alpha,
Dhaka, Bangladesh
<EMAIL_ADDRESS>&Fabliha Fairooz
Research and Development Department, Pioneer Alpha,
Dhaka, Bangladesh
<EMAIL_ADDRESS>&Sai Puppala
Department of Computer Science, University of Alabama at Birmingham
Alabama, USA
<EMAIL_ADDRESS>
&Sajedul Talukder
Department of Computer Science, University of Alabama at Birmingham
Alabama, USA
<EMAIL_ADDRESS>
###### Abstract

One way to expand the available dataset for training AI models in the medical
field is through the use of Generative Adversarial Networks (GANs) for data
augmentation. GANs work by employing a generator network to create new data
samples that are then assessed by a discriminator network to determine their
similarity to real samples. The discriminator network is taught to
differentiate between actual and synthetic samples, while the generator system
is trained to generate data that closely resemble real ones. The process is
repeated until the generator network can produce synthetic data that is
indistinguishable from genuine data. GANs have been utilized in medical image
analysis for various tasks, including data augmentation, image creation, and
domain adaptation. They can generate synthetic samples that can be used to
increase the available dataset, especially in cases where obtaining large
amounts of genuine data is difficult or unethical. However, it is essential to
note that the use of GANs in medical imaging is still an active area of
research to ensure that the produced images are of high quality and suitable
for use in clinical settings.

_K_ eywords Medical imaging, diagnosis, Generative Adversarial Networks,
augmentation, data generation.

##  1 Introduction

Data augmentation is an important technique in medical image analysis to
improve the robustness and generalization of models. Numerous studies have
applied data augmentation techniques, including Generative Adversarial
Networks (GANs), to medical images for generating realistic images. A few of
the popular examples are described by authors Abdelhalim [1], and Sun [27] in
their conference papers on how Generative Adversarial Network is useful for
data augmentation. Abdelhalim [1] in his conference papers explains about on
how to create generated pictures of skin lesions using a GAN to enhance the
data. On the other side Sun et al. [27] employs Generative Adversarial
Networks (GANs) to create artificial medical pictures for data augmentation
and shows how well it performs on a task to identify lung nodules. These
studies highlight the necessity for rigorous examination of the produced
pictures to assure their quality and appropriateness for clinical application,
in addition to the potential of GANs for data augmentation in medical image
analysis.

Figure 1 is representing the process of augmentation using GANN. In this paper
[24],the training and testing procedure is shown in Figure 1 utilizing both
actual data and data produced by the GAN. The input data is split into three
groups: training data, which makes up 70% of the total, testing data, which
makes up 30% of the total, and GAN data, which makes up 11.75% of the training
data and 8.2 percent of the fault machine data.

It is crucial to remember that data augmentation is just one aspect of
building robust medical image analysis models, and a thorough evaluation of
multiple techniques and models is needed for each specific task and dataset.
The most important component of AI applications is data. Lack of sufficient
labeled data frequently results in overfitting, which prevents the model from
generalizing to new samples. This can be lessened through data augmentation,
which effectively increases the volume and diversity of data that the network
observes. It is accomplished by applying changes, including such rotation,
cropping, shadowing, etc., to an initial dataset in order to artificially
create new data. However, figuring out which augmentations will be most
effective for the current dataset is not an easy process.

We must understand that data augmentation is just one aspect of building
robust medical image analysis models for creating pseudo-realistic images, and
extra evaluation of multiple techniques and models might be needed for each
specific task requirement and data set. The most important component of AI
applications is data. Lack of sufficient labeled data frequently results in
overfitting, which prevents the model from generalizing to new samples. This
can be lessened through data augmentation, which effectively increases the
volume and diversity of data that the network observes. We can accomplish data
augmentation by effectively applying needed changes to input images such as
rotation, cropping, shadowing, etc., to an original data set for possible
artificial data creation which resembles input images. However, figuring out
which augmentations will be most effective for the current data set is not an
easy process.

For many years, the method of data augmentation has been employed extensively
in machine learning and computer vision. By creating additional samples from
the existing ones, data augmentation aims to artificially expand the size of
the training dataset. This can lessen overfitting and increase the resilience
and generalizability of models. This work demonstrates the value of data
augmentation in enhancing the performance of deep learning algorithms in
computer vision tasks, as demonstrated by numerous recent studies in the field
of data augmentation such as Krizhevsky et al. [18]. In their paper, they
further explain the data augmentation methods usage like randomized cropping
and flipping to expand the ImageNet collection. Since then, data augmentation
has become a widely adopted technique in machine learning, with many studies
proposing new data augmentation methods and evaluating their performance on
various tasks. Some recent research works that emerged in data augmentation
are mentioned by Cubuk et al. [8], which explains work on learning enhancement
techniques from data. In their paper, they utilized reinforcement learning to
automatically learn optimal data augmentation strategies for a specific task.
Also, they propose a novel data augmentation technique based on the random
mixing of image patches from different samples. In conclusion, data
augmentation has a long history in machine learning and computer vision and it
is still an active field of study with new approaches and methodologies being
put out often. Pseudo images that resemble with original ones are generated by
following data augmentation methodologies and we can further enhance the
images with the input data by following the below-mentioned approaches:

Flipping: This involves flipping the image horizontally or vertically to
generate a new sample.

Rotation: This involves rotating the image by a random angle to generate a new
sample.

Scaling: This involves resizing the image to generate a new sample.

Translation: This involves shifting the image by a random offset to generate a
new sample.

Cropping: This involves randomly cropping a portion of the image to generate a
new sample.

Color augmentation: This involves randomly changing the brightness, contrast,
or saturation of the image to generate a new sample.

Mixup: This involves mixing two different images to generate a new sample.

Cutout: This involves randomly masking out a portion of the image to generate
a new sample.

These techniques have been widely used in computer vision and machine
learning, and be effective in improving the robustness and generalization of
models. The different data augmentation techniques in computer vision are
evaluated by some of the recent studies including a study by Xiao et al. [19],
Cubuk et al. [8], and Xiao et al. [19]. The research done by Xiao et al. [19]
in their paper talks about various data augmentation techniques, including
flipping, rotation, and scaling, on image classification tasks. These studies
demonstrate the importance of data augmentation in computer vision and machine
learning and highlight the need for careful evaluation of different data
augmentation techniques for each specific task and dataset.

A novel approach to data augmentation in computer vision has been presented,
and it is known as generative adversarial networks (GANs). A generator network
plus a discriminator network make up a GAN, a form of deep learning model. The
discriminator network is taught to discern between the created samples and the
genuine ones, while the generator network is trained to develop new samples
that are identical to the original ones. The generation and discriminator are
trained in an oppositional fashion, with the generator attempting to make
samples that can deceive the discriminator and the discriminator attempting to
accurately determine whether a sample is genuine or fabricated.

Figure 1: Distribution of various dataset types (a) Dataset with the
sufficient sample (b) Dataset with a poor sample size. [24]

One potential advantage of GANs for data augmentation is that they can
generate new samples that are diverse and representative of the original
dataset. The resilience and applicability of models developed using the
additional dataset may benefit from this approach. GANs could be very
beneficial for data augmentation in computer vision, and some of the recent
studies by Cubuk et al. [8], and Chen et al. [7] in their papers emphasized
it. Here, they suggest a GAN-based data augmentation approach for classifying
medical images and a Generative adversarial network data augmentation method
for segmenting images. Various works highlight the need for more investigation
and assessment of GANs in these fields by showcasing the promise of GANs for
data augmentation in computer vision and medical imaging analysis.

In [14] recent years, both medical image analysis and artificial intelligence
have experienced significant growth and development. The development of AI
models that can carry out a variety of tasks in computer-aided diagnoses, such
as classification tasks, segmentation techniques, image registration, and
image synthesis, has been made possible by the increasing accessibility of
massive data of medical images and advancements in deep learning algorithms.
These AI models have the potential to significantly impact healthcare by
providing faster, more accurate, and more cost-effective solutions for medical
image analysis. However, there are also important challenges that must be
addressed, including the need for high-quality annotated datasets, the
development of robust and interpretable models, and the need for careful
validation and evaluation of the models. Recent studies which specifically
evaluated the performance metrics of AI models in medical image analysis
discuss more these challenges. As presented by author Chen et al. [7] in their
paper, evaluate the performance of AI models for disease classification and
localization on a large dataset of chest X-rays. Also, author Chen et al. [7]
presents a deep learning architecture for pulmonary nodule detection in CT
images and evaluates its performance on a large dataset of medical images.
These studies demonstrate the potential of AI for medical image analysis and
highlight the need for continued research and development in this field.

This chapter will cover the lack of medical data and how generative
adversarial networks may assist to address it (GANs).

The shortage of data in the medical field presents a significant obstacle for
Artificial Intelligence and also emphasizes the challenges for medical
analysis. It can be time-consuming, costly, and susceptible to ethical and
legal restrictions to gather high-quality annotated medical photographs. This
may result in a lack of data for developing and testing AI models, which might
have an impact on their effectiveness and generalizability.

Figure 2: Distribution of various dataset types (a) Dataset with a sufficient
sample (b) Dataset with a poor sample size [24]

.

Several methods have been put out to improve medical data to solve this
problem, including generative models like Generational Adversarial Networks
(GANs) and Variational Auto-encoders (VAEs). This chapter will discuss the
problem of limited data and possible remedies utilizing GANs and other
variational auto-encoders (VAEs). These models may be used to create new
synthetic pictures that are comparable to the original photos after being
trained on a small dataset of medical images. The original dataset may then be
supplemented with synthetic pictures, expanding the amount and complexity of
the data for developing and testing AI models.

##  2 Literature Review

Medical image analysis and artificial intelligence (AI) have become
increasingly important in recent years for improving the accuracy and
efficiency of medical diagnosis and treatment [4]. AI algorithms can be
trained on large amounts of medical data to automatically detect and diagnose
medical conditions, such as diseases, tumors, and abnormalities, based on
medical images. The use of AI in medical image analysis has the potential to
revolutionize the healthcare industry by providing more accurate and timely
diagnoses, reducing the workload of medical professionals, and improving
patient outcomes.

Some examples of AI applications in medical image analysis include computer-
aided diagnosis (CAD), image segmentation, image registration, and image
synthesis. CAD involves using AI algorithms to assist medical professionals in
making diagnoses based on medical images. Image segmentation involves
separating an image into different regions or objects of interest. Image
registration involves aligning and matching different images of the same
patient over time. Image synthesis involves generating new images from
existing images for data augmentation, improved visualization, and model
training. Several studies have demonstrated the potential of AI in medical
image analysis and have shown promising results in various applications.

###  2.1 Artificial Intelligence in the Context of Medical Images

The effective utilization of artificial intelligence (AI) on medical images is
crucial for realizing the potential benefits of AI in healthcare, such as
improved accuracy and efficiency of medical diagnosis and treatment. To
achieve this, several factors must be considered, including data quality,
model design, and evaluation methods. Data quality is a key factor in the
effective utilization of AI in medical images. Medical images should be of
high quality and accurately annotated to ensure that AI algorithms can
effectively learn from the data. Additionally, large amounts of medical data
are needed to train and evaluate AI models, and the data should be diverse and
representative of the population of interest. As presented by author Cai et
al. [6] in their paper, where they propose a deep learning-based computer-
aided diagnosis system and demonstrate its effectiveness on a benchmark
dataset. Additionally, Chen et al. [7] in his paper compares the performance
of fully trained deep convolutional neural networks (DCNNs) and fine-tuned
DCNNs for medical image analysis and demonstrates the effectiveness of fine-
tuning for improved performance.

Model design is another important factor in the effective utilization of AI on
medical images. AI models should be designed with the specific application in
mind, taking into account the type of medical image and the desired output.
For example, AI models for medical image segmentation should be designed with
the ability to accurately distinguish between different regions or objects of
interest in an image. Evaluation methods are also critical for the effective
utilization of AI in medical images. For more details about it, we can look
into some recent studies where author Cai et al. [6] compare the performance
of various deep learning algorithms for medical image segmentation and
demonstrate the effectiveness of deep learning for this application. In [13] a
collaborative federated learning system that enables deep-learning image
analysis and classifying diabetic retinopathy without transferring patient
data between healthcare organizations has been introduced. The models
generated using artificial intelligence should be evaluated on independent
datasets to ensure their generalizability and reliability. Additionally, the
evaluation metrics used should be relevant to the specific application, we can
consider model evaluation metrics such as sensitivity and specificity for
medical diagnosis, and the Dice similarity coefficient for medical image
segmentation. These studies demonstrate the potential of AI for the effective
utilization of medical images in healthcare and highlight the importance of
considering data quality, model design, and evaluation methods.

###  2.2 Issue of Scarcity of Medical Data

The scarcity of medical data is a major challenge in the field of healthcare
and medical research. Medical data refers to the information generated by
medical devices, like imaging devices, and other sources, like electronic
health records by author Nasim et al [26]. The scarcity of medical data can
limit the ability of medical professionals to make accurate diagnoses and
develop effective treatments, as well as hinder the development of new medical
technologies and research.

The primary reason for data scarcity in the medical field might include data
privacy concerns, limited data sharing between institutions, and the cost of
data collection and storage. Data privacy concerns make it difficult for
medical researchers and companies to access large amounts of medical data for
their research and development activities. Limited data sharing between
institutions also restricts the availability of medical data, as each
institution has its own data collection, storage, and access policies.
Finally, the cost of data collection and storage can be prohibitive for many
institutions, particularly for those with limited resources. The images
available in the medical field are far fewer as compared to other forms of
data, such as text and numerical data, for many reasons, some are briefly
mentioned below:

Data Privacy Concerns: Medical images often contain sensitive personal
information and are subject to strict privacy regulations, such as the Health
Insurance Portability and Accountability Act (HIPAA) in the United States.
This can make it difficult for researchers and organizations to access large
amounts of medical image data [30].

Cost of Data Collection and Storage: Medical imaging, such as MRI and CT
scans, require specialized equipment and trained personnel, and the cost of
acquiring and storing these images can be high. This limits the number of
medical images that can be collected and stored.

Limited Data Sharing between Institutions: Each institution is entitled to its
data and they have different policies to access these records. The primary
reason for having these policies is to secure medical information as it is
very personal and institution likes to keep it confidential. Accessing these
data might be very tedious and time-consuming even for researchers.

Annotation Requirements: Medical images often require manual annotation by
medical experts, which can be time-consuming and costly. The need for accurate
annotations also limits the number of medical images that can be used for
training and evaluating machine learning models.

Despite these challenges, efforts are being made to increase the amount of
medical image data available, such as data augmentation, synthetic data
generation, and federated learning. These approaches aim to overcome the
limitations of the scarce amount of medical images and enable the effective
utilization of AI in healthcare.

Data augmentation involves generating new data from existing data, while
synthetic data generation involves creating artificial data that mimics real-
world data. Federated learning involves training machine learning models on
multiple institutions’ data without actually sharing the data, thereby
addressing privacy concerns. Here are a few advancements in federated learning
[28, 29, 21].

Despite these efforts, the scarcity of medical data remains a major challenge
and requires ongoing research and development to overcome. This is important
for improving medical diagnosis and treatment, as well as advancing the field
of medical research and technology.

Table 1: Related work in a nutshell Reference |  Publication Year |  Workflow |  Outcome
---|---|---|---
[7] |  2022 |  The goal of this study is to synthesize several retinal (or neuronal) pictures with realistic appearances from a tubular structured annotation that is hidden and contains the binary vascular (or neuronal) shape. It was inspired by current developments in generative adversarial networks (GANs) and visual style transfer. Required 10 training instances. |  The effectiveness of the suggested strategy is supported by extensive experimental evaluations on several retinal fundus and neural imaging applications.
[12] |  2022 |  Researchers have enhanced classification by supplementing data using noise-to-image or image-to-image GANs, which may synthesis realistic/diverse extra training pictures to replace the data gap in the true image distribution. Two-step GAN-based DA that creates and improves brain Magnetic Resonance (MR) pictures in order to enhance the DA impact using the GAN combinations. |  Two-step GAN-based DA can greatly outperform the conventional DA alone in tumor identification (raising sensitivity from 93.67% to 97.48%).
[22] |  2015 |  The authors suggest testing the conditional distributions learnt by applying common classification criteria to a conditional version of our model. Using a nearest neighbor classifier to compare actual data to a collection of artificially created conditional samples, we trained a DCGAN using MNIST (splitting off a 10K validation set) and a permutation invariant GAN baseline. |  The DCGAN model successfully models the conditional distributions of this dataset, as evidenced by its test error being equal to that of a closest neighbor classifier fitted on the training dataset. The test error for 10M samples is 1.48%.
[5] |  2018 |  The use of GAN-derived synthetic pictures to supplement training data has been examined in this article, and it has been shown to increase performance on two segmentation tasks. The strategy has been demonstrated to perform well in situations with sparse data, whether due to a dearth of accurate data or as a result of class inequality. |  According to a cautious interpretation of the findings from the usual tasks investigated here, supplementing 5–50 labelled picture volumes with an extra 10–100% GAN-derived synthetic patches has the potential to significantly enhance DSC.

###  2.3 Data Augmentation

Image augmentation is a technique used to artificially increase the size of a
dataset by generating new images based on existing ones. The purpose of image
augmentation is to reduce overfitting, improve generalization, and increase
the robustness of machine learning models when applied to image recognition
tasks. Image augmentation is commonly used in computer vision and medical
imaging. There are various types of image augmentation techniques, including
rotation, scaling, flipping, cropping, and color transformation. These
techniques can be applied to images randomly or in a controlled manner to
generate new, augmented images. The choice of augmentation techniques depends
on the nature of the task and the type of data being used. Image augmentation
is very useful for many reasons. Some of the common problems while dealing
with deep learning or machine learning algorithms are Overfitting and
Underfitting, Overfitting occurs when a machine learning model is too closely
fit to the training data and performs poorly on new, unseen data. Image
augmentation can help reduce overfitting by generating new, augmented images
that can be used to train the model, making it more robust and generalizable.
Generating these pseudo images using data augmentation techniques based on
existing ones could help improve the generalization of machine learning models
to new, unseen data. This can increase the robustness of the models and reduce
the risk of poor performance on real-world data. Image augmentation can be
used to simulate real-world scenarios, such as changes in lighting conditions,
and to test the robustness of machine learning models to these conditions.
This can help ensure that the models are suitable for deployment in real-world
applications. Image augmentation can also be used to balance the distribution
of classes in a dataset, which can be important for avoiding bias in machine
learning models.

In medical imaging, image augmentation is used to address the scarcity of
annotated medical images. Image augmentation is also applied in the clinical
imaging field to address the scarcity of annotated medical images. In clinical
imaging, it is important to have large datasets of annotated images to train
machine-learning models for various tasks, such as disease diagnosis, lesion
segmentation, and treatment planning. However, obtaining annotated medical
images can be challenging due to ethical and logistical reasons. Image
augmentation can also be used to simulate real-world scenarios, such as
changes in lighting conditions, and to test the generalization of models to
new, unseen data. Overall, image augmentation is a powerful tool for
addressing the scarcity of data in computer vision and medical imaging, and
has the potential to improve the performance and robustness of machine
learning models applied to these fields.

The use of Generative Adversarial Networks (GANs) for picture-generating tasks
is very common. The purpose of picture creation is to create artificial
visuals that resemble genuine photos. The generator is instructed to generate
synthetic pictures that are identical to genuine images, while the classifier
is trained to discriminate between actual and synthetic images.

In the [2] realm of image generation, GANs have been applied to a variety of
tasks, including creating fake pictures of people, things, and environments.
These applications have proven that GANs are capable of producing high-quality
synthetic pictures that closely resemble genuine photos and are efficient at
generating images that are hard to get or are not readily available in huge
quantities.

###  2.4 Concept of General Adversarial Network

The role of a Generative Adversarial Network is broadly divided into two parts
namely, A generator and a discriminator in deep learning architecture as
mentioned by the author Ali et al. [3]. The discriminator is taught to
distinguish between the synthetic pictures produced by the generator and
actual photos from the target dataset, while the generator is trained to
produce artificial images that are comparable to a target dataset.

The discriminator seeks to reliably determine whether a picture is genuine or
synthetic, while the generator strives to create artificial images that are
indistinguishable from actual photos. The generator and discriminator are
trained together in a game considering. While the discriminator gets better at
telling the difference between actual and fake photos, the generator gets
better at creating fake images that seem like real ones with time as presented
by author Sauber-Cole et al. [25]

The use of Generational Adversarial Networks (GANs) for picture-generating
tasks is very common. The purpose of picture creation is to create artificial
visuals that resemble genuine photos. The generator is trained to produce
synthetic pictures that are indistinguishable from genuine images, while the
discriminator is trained to discriminate between actual and synthetic images.
These applications have proven that GANs are capable of producing high-quality
synthetic pictures that closely resemble genuine photos and are efficient at
generating images that are hard to get or are not readily available in huge
quantities. These synthetic images can be used to augment the size of the
dataset, improving the accuracy and robustness of machine learning models.

Figure 3: Distribution of various dataset types (a) Dataset with the
sufficient sample (b) Dataset with a poor sample size.[24]

Yet, conventional data augmentation techniques only yield a small amount of
credible alternative data. The performance of CNNs has been enhanced by the
use of Generative Adversarial Networks (GANs), which produce fresh data. Yet,
compared to CNNs, data augmentation methods for training GANs are less
studied. In this study [20], authors suggest a novel GAN architecture for
augmenting chest X-rays for semi-supervised COVID-19 and pneumonia
identification. The suggested GAN may be utilized to efficiently augment data
and enhance illness classification accuracy in chest X-rays for pneumonia and
COVID-19, according to the authors’ research. The construction of the
suggested IAGAN’s Generator is seen in Fig. 3. The generator (G) receives a
batch of actual training images and a Gaussian noise vector as input at each
cycle. Authors aim to not only use the full image representation using the
discriminator, but also to get a lower representation of images fed through
the generator for better generalizability of G in generating image data by
first encoding the input images using convolution and attention layers to a
lower-dimensional representation, before concatenating this representation of
the image with the projected noise vector (concatenation happens after goes
through a dense layer and non-linearity). The trained generator may employ
photos from several classes and produce a wider variety of images thanks to
the dual input to the generator.

Once the training process is complete, the generator can be used to generate
synthetic images that are similar to the target dataset. These synthetic
images can be used to augment the size of the dataset, improving the accuracy
and robustness of machine learning models. GANs have been applied to a wide
range of applications, including image generation, data augmentation, and
semi-supervised learning [15]. Overall, GANs are a powerful tool for
synthesizing new, synthetic images that are similar to real images and are
effective for data augmentation in various fields, including medical imaging.

###  2.5 GAN concept for medical images

Generative Adversarial Networks (GANs) is one of the popular deep learning
architecture which has been used effectively in several fields, including
medical imaging. By creating artificial pictures that closely resemble genuine
images, GANs can be employed in medical imaging to supplement the little
quantity of annotated clinical data available as stated by author Goodfellow
in his book [11].

A generator and a discriminator are the two primary parts of a GAN elaborately
explained in his paper Han et al. [33]. The generator is trained to produce
synthetic pictures that are indistinguishable from genuine images, Whereas the
discriminator tries to discern the difference between actual and fake
pictures, the generator creates synthetic ones. The generator and
discriminator are taught in an antagonistic way, with the generator attempting
to deceive the discriminator into believing the fake pictures are genuine and
the discriminator attempting to accurately identify the true images. GANs can
be used to supplement the training data, boosting its variety and lowering the
likelihood of overfitting. The employment of artificial pictures may also be
utilized to investigate the latent region of the distribution of data and
assess how reliable computer vision algorithms are.

GANs have been used for a variety of tasks in medical imaging, including image
synthesis, super-resolution, image-to-image translation, and data
augmentation. GANs have been used, for instance, to convert MR pictures across
different modalities and to create high-resolution MR images with low-
resolution MR images [2]. It is significant to remember that both the richness
of the training data and the intricacy of the generator structure affect the
accuracy and realism of the synthetic pictures produced by GANs as discussed
by Radford in his paper about GAN with unsupervised representation learning
[22]. Additionally, GANs are heavily data-driven and may not adapt well to
additional data distributions, particularly in medical imaging where the data
can be quite diverse and sensitive to inter-patient variability.

##  3 Methodology

A significant obstacle in the realm of medical image analysis is the dearth of
medical data. The purpose of this study is to investigate the usage of
variational auto-encoders (VAEs) and generative adversarial networks
(Generative adversarial) to improve medical data [17]. The literature on the
application of GANs and VAEs for data augmentation will be thoroughly
reviewed. This review will focus on the use of these methods in the area of
medical image analysis and will cover recent research publications, technical
reports, and conference proceedings.

###  3.1 Data Collection

For experimentation, the project will employ a publicly accessible medical
imaging collection, such as the MRI, CT Scan, or Chest X-Ray dataset. One
portion of the dataset will be utilized for training and the other portion for
testing. Data collection is a primary and crucial step before we train our
deep learning model using generative adversarial networks (GANs). The GANs’
effectiveness and capacity to produce plausible synthetic data can be
considerably impacted by the caliber and diversity of the data used to train
them, according to the author, Goodfellow et al. [11]. The method of gathering
data for GANs involves numerous steps. The selection of the data is also
crucial. Choosing the pertinent data for the task at hand is the first step. A
publicly accessible medical images collection, like the MNIST or the Chest
X-Ray dataset, might be employed in the case of healthcare image analysis.
Making sure that the information gathered is diverse and appropriate for the
task at hand is crucial. The GANs will be better able to generalize and
provide high-quality synthetic data as a result.

###  3.2 MRI Pre-processing

Once the data has been selected, it needs to be preprocessed to prepare it for
training. This may involve resizing the images to a standard size, normalizing
the pixel values, and removing any irrelevant information from the images.
Image preprocessing is an important step before training Generative
Adversarial Networks (GANs) as it can significantly impact the performance and
ability of the GANs to generate realistic synthetic data by referred by Karras
in his paper about analyzing and improving image quality [16].

###  3.3 Workflow of GAN for Data Augmentation

To implement the GANs and VAEs, deep learning frameworks like TensorFlow or
PyTorch will be used. The training dataset will be used to train the GANs and
VAEs, and the testing dataset will be used to assess their performance. The
preprocessed data may be used with data augmentation techniques to add
variation to the training data and to guard against overfitting. Random
picture flips, translations, and rotations are common data augmentation
techniques.

A training set as well as a validation set should be created from the
preprocessed and enhanced data. The validation set is used to assess how well
the GANs performed while they were being trained, whereas the training set is
used to train the GANs.

Figure 4: The model training process pipeline. According to the supplied
picture, the generator first creates ground glass nodules from the backdrop.
Second, to determine if the synthetic picture is real or not, either region of
interest (ROI) discriminative model (red line) and the entire image
discriminator (blue line) extract the features from the ROI and complete
image, respectively. [31] Figure 5: The network’s structure. In the input
mask’s place, the generator produces a synthetic ground glass nodule. The
batch normalization, the "parametric rectified linear unit" (PReLU) activation
function, and convolutional layers with a 3 3 kernel size make up the
generator. The batch normalization, the leaky PReLU activation function, and
the 3 3 kernel size convolutional layers made up the discriminator. [31]

####  3.3.1 General Algorithm.

A generator and a discriminator are the two primary parts of the deep learning
technique known as generative adversarial networks (GANs). To create fake data
that mimics genuine data, these elements are trained in an adversarial way.

The basic steps involved in a GAN algorithm are:

1\. Data Preparation: The training data is collected and preprocessed.

2\. Generator: The generator is a neural network that maps a random noise
vector to a synthetic data sample. The generator is trained to generate
synthetic data that resembles real data.

3\. Discriminator: The discriminator is a neural network that distinguishes
between real and synthetic data. The discriminator is trained to correctly
identify real data and to reject synthetic data.

4\. Adversarial Training: The generator and the discriminator are trained
simultaneously, in an adversarial manner. The generator tries to generate
synthetic data that the discriminator cannot distinguish from the real data,
while the discriminator tries to correctly identify the real data and reject
the synthetic data generated by the generator.

5\. Synthetic Data Generation: After the training process is completed, the
generator can be used to generate synthetic data that resembles real data.

The quality of the synthetic data generated by a GAN depends on the complexity
of the generator and discriminator, the quality of the training data, and the
number of iterations performed during the training process.

###  3.4 The Process of Data Augmentation Using GAN

Generative Adversarial Networks (GANs) perform data augmentation by
synthesizing new, artificial data samples that resemble the real data [32].
The GAN consists of two main components: a generator and a discriminator. The
generator is a neural network that maps a random noise vector to a synthetic
data sample.

The discriminator is a neural network that distinguishes between real and
synthetic data. The discriminator is trained to correctly identify real data
and to reject synthetic data. The generator and the discriminator are trained
simultaneously, in an adversarial manner. The generator tries to generate
synthetic data that the discriminator cannot distinguish from the real data,
while the discriminator tries to correctly identify the real data and reject
the synthetic data generated by the generator as presented by author Bowles et
al. [5]. After the training process is completed, the generator can be used to
generate synthetic data that resembles real data. This synthetic data can be
used to augment the real data, increasing the size of the training data set,
and improving the performance of machine learning algorithms trained on this
data.

###  3.5 Data Augmentation Using Variational Auto-Encoders (VAEs)

Variational Autoencoders (VAEs) can be used for data augmentation by
generating synthetic data samples from the same underlying distribution as the
real data. VAEs consist of an encoder network that maps the input data to a
lower-dimensional representation, and a decoder network that maps the lower-
dimensional representation back to the original data space [23]. The encoder
and decoder are trained together in an unsupervised manner, such that the
decoder can reconstruct the original data from the lower-dimensional
representation.

Figure 6: General description of the suggested technique. [10]

To use VAEs for data augmentation, the encoder-decoder architecture can be
used to generate synthetic data samples as presented by author Garay-Maestre,
[10]. This can be done by sampling from the prior distribution over the lower-
dimensional representation, and then passing this sample through the decoder
network to obtain a synthetic data sample in the original data space.

The one advantage of VAE as compared to that of GAN for data augmentation is
that they can generate synthetic data that is similar to the real data in
terms of structure and distribution as explained by author Fuertes, [9]. This
can help improve the performance of machine learning algorithms by increasing
the size of the training data set and helping to avoid overfitting.

###  3.6 Result Analysis

The results of the study will be analyzed and discussed in terms of the
performance of the GANs and VAEs for data augmentation. The results will be
compared to existing approaches for data augmentation, and the advantages and
disadvantages of the GANs and VAEs will be discussed. The study will conclude
with a summary of the findings and a discussion of the implications of the
results for the field of medical image analysis. Recommendations for future
research in this area will also be provided.

Generative Adversarial Networks (GANs) are effective for image augmentation
because they can generate synthetic data that resembles real data. This
synthetic data can be used to augment the real data, increasing the size of
the training data set and improving the performance of machine learning
algorithms trained on this data.

GANs can generate high-quality synthetic data because they are trained on real
data. The generator network maps a random noise vector to a synthetic data
sample, while the discriminator network is trained to distinguish between real
and synthetic data. The generator and discriminator are trained simultaneously
in an adversarial manner, with the generator trying to generate synthetic data
that the discriminator cannot distinguish from real data, and the
discriminator tries to correctly identify real data and reject synthetic data.

This adversarial training process results in the generator learning to
generate synthetic data that closely resembles the real data, and the
discriminator learning to correctly identify real data. As a result, the
synthetic data generated by the GAN can be used to augment the real data and
increase the size of the training data set, leading to improved performance of
machine learning algorithms.

##  4 Conclusion

Generative Adversarial Networks (GANs) may be utilized for data augmentation
for creating synthetic samples to increase the size of the training dataset.
In this procedure, new data samples are generated by a generator network and
then assessed by a discriminator network to see whether they are sufficiently
comparable to the original samples. While the discriminator network is taught
to discriminate between actual and created samples, the generator network is
trained to increase its capacity to generate data that are comparable to real
ones. The enhanced dataset may then be used to train a model for the desired
objective. The procedure is repeated until the generator network can generate
synthetic samples that are indistinguishable from genuine ones. Medical image
analysis has used Generative Adversarial Networks (GANs) for a variety of
tasks, including data augmentation, picture creation, and domain adaptation.
In the field of medical imaging, GANs can create synthetic samples that can be
utilized to increase the dataset that is already accessible, especially when
collecting significant volumes of actual data is challenging or morally
problematic. It is important to emphasize that research into the application
of GANs in radiography is still ongoing, and a thorough evaluation of the
produced pictures is necessary to assure their quality and appropriateness for
clinical applications.

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#  12th Workshop on the CKM Unitarity Triangle
Santiago de Compostela, 18-22 September 2023
CP violation measurements in three-body charmless B decays

Lucas<EMAIL_ADDRESS>on behalf of the LHCb Collaboration
1Centro Brasileiro de Pesquisas Físicas  Rio de Janeiro  Brazil

###### Abstract

This study reports CP asymmetry measurements in charmless three-body decays of
B mesons, both using the Run II data from proton-proton collisions at a
center-of-mass energy of 13 $TeV$, collected by the LHCb detector from 2015 to
2018, with an integrated luminosity of 5.9 $fb^{-1}$. Significant CP
asymmetries are observed in $B^{\pm}\rightarrow\pi^{\pm}\pi^{+}\pi^{-}$ and
$B^{\pm}\rightarrow K^{\pm}K^{+}K^{-}$ decays, while the previously observed
asymmetry in $B^{\pm}\rightarrow\pi^{\pm}K^{+}K^{-}$ decays is confirmed, and
the CP asymmetry of $B^{\pm}\rightarrow K^{\pm}\pi^{+}\pi^{-}$ decays is found
to be compatible with zero [1]. Additionally, a new method is used to measure
the CP asymmetry in charmless $B\rightarrow PV$ decays, showing significant
asymmetry in $B^{\pm}\rightarrow K^{\pm}\pi^{+}\pi^{-}$ decays dominated by
$\rho(770)$, representing the first observation of CP violation in this
process. Other decay channels show CP asymmetries compatible with zero [2].

##  1 Direct CP violation in charmless three-body decays of $B^{\pm}$ mesons

The decays of heavy mesons into three-body can be comprehended by considering
a combination of intermediate states featuring resonances. Within this
framework, the CP-integrated asymmetries ($A_{CP}$) observed in charmless
three-body decays of B mesons are the sum of asymmetries associated with each
intermediate state, weighted by their respective fractions. This study
investigates CP violation in four charmless B meson decays involving three
charged pseudoscalar particles: $B^{\pm}\rightarrow K^{\pm}\pi^{+}\pi^{-}$,
$B^{\pm}\rightarrow K^{\pm}K^{+}K^{-}$,
$B^{\pm}\rightarrow\pi^{\pm}K^{+}K^{-}$, and
$B^{\pm}\rightarrow\pi^{\pm}\pi^{+}\pi^{-}$. The analysis focuses on measuring
phase-space integrated CP asymmetries and asymmetries within specific regions
of the Dalitz plots. To take into account the production asymmetries of the B
mesons, the decay $B^{\pm}\rightarrow
J/\psi(\rightarrow\mu^{+}\mu^{-})K^{\pm}$ is employed as a control channel.

The yield and raw asymmetry of each decay are derived through simultaneous
unbinned extended maximum likelihood fits to the $B^{\pm}$ invariant mass
distributions, as shown in Fig. 1. Signal components within each of the four
channels are modeled by a combination of a Gaussian and two Crystal Ball
functions, sharing a common mean. Combinatorial backgrounds are parametrized
by an exponential function. Backgrounds arising from partially reconstructed
four-body B decays are represented by the ARGUS function [3] convolved with a
Gaussian resolution function. Simulated event samples are used to model
detector efficiency effects. The resultant signal yields and raw asymmetries
($A_{raw}$) are outlined in Table 1.

Figure 1: Invariant mass spectra of the four decays in the range [5050–5650] MeV/$c^{2}$. Table 1: Signal yields, asymmetries, and efficiency ratio R of the four decays. Decay mode | Total yield | $A_{raw}$ | $R=\langle\epsilon^{-}\rangle/\langle\epsilon^{+}\rangle$
---|---|---|---
$B^{\pm}\rightarrow K^{\pm}\pi^{+}\pi^{-}$ | $499200\pm 900$ | $+0.006\pm 0.002$ | $1.0038\pm 0.0027$
$B^{\pm}\rightarrow K^{\pm}K^{+}K^{-}$ | $365000\pm 1000$ | $-0.052\pm 0.002$ | $0.9846\pm 0.0024$
$B^{\pm}\rightarrow\pi^{\pm}\pi^{+}\pi^{-}$ | $101000\pm 500$ | $+0.090\pm 0.004$ | $1.0354\pm 0.0037$
$B^{\pm}\rightarrow\pi^{\pm}K^{+}K^{-}$ | $32470\pm 300$ | $-0.132\pm 0.007$ | $0.9777\pm 0.0032$

###  1.1 Phase-space integrated and localized CP asymmetries

Phase-space integrated CP asymmetries are derived by correcting the raw
asymmetry for selection efficiency effects to obtain the efficiency-corrected
raw asymmetries ($A^{corr}_{raw}$), and considering the production asymmetry
($A_{P}$). The physical CP asymmetry can be expressed in terms of
$A^{corr}_{raw}$ and $A_{P}$:

$A_{CP}=\frac{A^{corr}_{raw}-A_{P}}{1-A^{corr}_{raw}A_{P}}.$ (1.1)

The CP asymmetries of the four $B^{\pm}\rightarrow
h^{\pm}h^{\prime+}h^{\prime-}$ modes are measured to be:

$\displaystyle A_{CP}(B^{\pm}\rightarrow K^{\pm}\pi^{+}\pi^{-})=+0.011\pm
0.002\pm 0.003\pm 0.0003,$ (1.2) $\displaystyle A_{CP}(B^{\pm}\rightarrow
K^{\pm}K^{+}K^{-})=-0.037\pm 0.002\pm 0.002\pm 0.003,$ $\displaystyle
A_{CP}(B^{\pm}\rightarrow\pi^{\pm}\pi^{+}\pi^{-})=+0.080\pm 0.004\pm 0.003\pm
0.003,$ $\displaystyle A_{CP}(B^{\pm}\rightarrow\pi^{\pm}K^{+}K^{-})=-0.114\pm
0.007\pm 0.003\pm 0.003.$

The first uncertainty is statistical, followed by systematic uncertainty, with
a third component stemming from the limited knowledge of the CP asymmetry in
the control channel [1]. The computation of significance of CP asymmetries is
8.5$\sigma$ for $B^{\pm}\rightarrow K^{\pm}K^{+}K^{-}$ decays, 14.1$\sigma$
for $B^{\pm}\rightarrow\pi^{\pm}\pi^{+}\pi^{-}$ decays, and 13.6$\sigma$ for
$B^{\pm}\rightarrow\pi^{\pm}K^{+}K^{-}$ decays, marking the first observation
of CP asymmetries in these decay channels. In the case of $B^{\pm}\rightarrow
K^{\pm}\pi^{+}\pi^{-}$ decays, the CP asymmetry significance is 2.4$\sigma$,
aligning with the expectation of CP conservation.

The so-called Dalitz plot represents directly the decay dynamics, allowing the
examination of various resonant and non-resonant components to scrutinize CP
asymmetry effects. To visualize localized asymmetries, the $A_{CP}$ is
calculated in bins of the phase space [4].

Figure 2: Asymmetry distribution in bins of the Dalitz plot for (a)
$B^{\pm}\rightarrow\pi^{\pm}\pi^{+}\pi^{-}$; (b) $B^{\pm}\rightarrow
K^{\pm}\pi^{+}\pi^{-}$; (c) $B^{\pm}\rightarrow\pi^{\pm}K^{+}K^{-}$; and (d)
$B^{\pm}\rightarrow K^{\pm}K^{+}K^{-}$.

For $B^{\pm}\rightarrow K^{\pm}\pi^{+}\pi^{-}$, the $m^{2}(K^{+}\pi^{-})$ axis
reveals a change in the asymmetry sign, see (b) in Figure 2. For the
rescattering region of $B^{\pm}\rightarrow\pi^{\pm}K^{+}K^{-}$, one can see a
nearly constant CP asymmetry along the $m^{2}(K^{+}\pi^{-})$ axis, see (c).
The $B^{\pm}\rightarrow K^{\pm}K^{+}K^{-}$ decays also reveal CP asymmetries
in the $m^{2}(K^{+}K^{-})$ projections in the rescattering region, and another
change in the asymmetry sign is observed, in an opposite direction with
respect to $B^{\pm}\rightarrow K^{\pm}\pi^{+}\pi^{-}$, see (d). A large
asymmetry is observed for the $B^{\pm}\rightarrow\pi^{\pm}\pi^{+}\pi^{-}$,
where there is an indication of $\chi_{c}^{0}(1P)$ contribution around 11.6
Ge$V^{2}/c^{4}$, see (a).

##  2 Search for direct CP violation in charged charmless $B\to PV$ decays

This study investigates quasi-two-body B meson decays into PV (pseudoescalar
and vector) final states, leading to three-body configurations arising from
the subsequent decays of the vector meson.

A novel approach, independent of a full amplitude analysis, is employed. The
method is based on three key features of three-body B decays [2]: the large
phase space; the dominance of scalar and vector resonances with masses below
or around 1 GeV/$c^{2}$; and the signatures of the resonant amplitudes in the
Dalitz plot.

Usually, the decay amplitudes of $B^{\pm}$ are represented as a coherent sum
of intermediate amplitudes, where the magnitude and phase of each amplitude
are adjustable parameters. When a single vector resonance interferes with a
scalar component, the decay amplitudes can be expressed as
$\mathcal{M}_{\pm}=a_{\pm}^{V}e^{i\delta^{V}_{\pm}}F_{V}^{BW}cos\theta(s_{\perp},s_{\parallel})+a_{\pm}^{S}e^{i\delta^{S}_{\pm}}F_{S}^{BW}$.
The Matrix element squared is:

$\displaystyle|\mathcal{M}_{\pm}|^{2}=(a^{V}_{\pm})^{2}(cos\theta)^{2}|F_{V}^{BW}|^{2}+(a^{S}_{\pm})^{2}|F_{S}^{BW}|^{2}+2a^{V}_{\pm}a^{S}_{\pm}cos\theta|F_{V}^{BW}|^{2}|F_{S}^{BW}|^{2}$
(2.1)
$\displaystyle\times\big{\\{}cos(\delta^{V}_{\pm}-\delta^{S}_{\pm})[(m^{2}_{V}-s_{\parallel})(m^{2}_{S}-s_{\parallel})+(m_{V}\Gamma_{V})(m_{S}\Gamma_{S})]$
$\displaystyle+sin(\delta^{V}_{\pm}-\delta_{\pm}^{S})[(m_{S}\Gamma_{S})(m^{2}_{V}-s_{\parallel})-(m_{V}\Gamma_{V})(m^{2}_{S}-s_{\parallel})]\big{\\}},$

where $a^{V}_{\pm}$ and $\delta^{V}_{\pm}$ are the magnitude and phase of the
vector and $a^{S}_{\pm}$ and $\delta^{S}_{\pm}$ are the magnitude and phase of
the scalar resonances. The $\theta(s_{\perp},s_{\parallel})$ is the helicity
angle. For the low mass and sufficiently narrow resonances, a parabolic
dependence of $cos\theta$ only on $s_{\perp}$ is a good approximation.
Assuming that the phases and amplitudes do not depend on $s_{\perp}$, the
Eq.2.1 can be reduced to a quadratic polynomial in
$cos\theta(m^{2}_{V},s_{\perp})$ as:

$|\mathcal{M}_{\pm}|^{2}=f(cos\theta(m^{2}_{V},s_{\perp}))=p_{0}^{\pm}+p_{1}^{\pm}cos\theta(m_{V}^{2},s_{\perp})+p_{2}^{\pm}cos^{2}\theta(m_{V}^{2},_{\perp})$
(2.2)

Given that the decay rates are proportional to $|\mathcal{M}_{\pm}|^{2}$, the
CP asymmetry $A_{CP}^{V}$ in $B\rightarrow PV$ is given as function of
$p_{2}^{\pm}$:

$A_{CP}^{V}=\frac{|\mathcal{M}_{-}|^{2}-|\mathcal{M_{+}}|^{2}}{|\mathcal{M_{-}}|^{2}+|\mathcal{M_{+}}|^{2}}=\frac{p_{2}^{-}-p_{2}^{+}}{p_{2}^{-}+p_{2}^{+}},$
(2.3)

Given the approximation $cos\theta(s_{\parallel},s_{\perp})\approx
cos\theta(m^{2}_{V},s_{\perp})$, $cos\theta$ becomes a linear function of
$s_{\perp}$ [5], with this approximation the CP asymmetry can be obtained from
the distribution of $s_{\perp}$. The function on Eq. 2.3 is used to fit the
histograms of data projected onto $s_{\perp}$ axes in order to determine the
$p_{0,1,2}^{\pm}$ and then calculate the $A_{CP}^{V}$.

Figure 3: Distribution of $s_{\perp}$ for $B^{\pm}$ and the corresponding
quadratic fits for (a) $\rho(770)$ in
$B^{\pm}\rightarrow\pi^{\pm}\pi^{+}\pi^{-}$, (b) $\rho(770)$ in
$B^{\pm}\rightarrow K^{\pm}\pi^{+}\pi^{-}$, (c) $K^{*}(892)^{0}$ in
$B^{\pm}\rightarrow K^{\pm}\pi^{+}\pi^{-}$, (d) $K^{*}(892)^{0}$ in
$B^{\pm}\rightarrow\pi^{\pm}K^{+}K^{-}$, and (e) $\phi(1020)$ in
$B^{\pm}\rightarrow K^{\pm}K^{+}K^{-}$

The efficiency-corrected yields of $B^{\pm}$ as a function of $s_{\perp}$ can
be seen in Fig. 3, with the results of the polynomial function from Eq. 2.3.
The impact of a CP conservation is evident in almost all of the plots, where
the vector parabolas of $B^{+}$ and $B^{-}$ are very close. The
$B^{\pm}\rightarrow\rho(770)^{0}K^{\pm}$ channel stands out from the others as
the only one which shows a significant CP asymmetry, measured to be
$A_{CP}=+0.150\pm 0.019$ with a statistical-only significance of 7.9 standard
deviations. For the other channels, the results are:
$A_{CP}(\rho(770)^{0}\pi^{\pm})=-0.004\pm 0.017$,
$A_{CP}(K^{*}(892)^{0}\pi^{\pm})=-0.015\pm 0.021$,
$A_{CP}(K^{*}(892)^{0}K^{\pm})=0.007\pm 0.054$,
$A_{CP}(\phi(1020)K^{\pm})=0.004\pm 0.014$.

##  3 Summary

Two interesting analyses are presented in this document. The first one showed
the inclusive CP asymmetries of the $B^{\pm}\rightarrow
K^{\pm}\pi^{+}\pi^{-}$, $B^{\pm}\rightarrow\pi^{\pm}K^{+}K^{-}$,
$B^{\pm}\rightarrow K^{\pm}K^{+}K^{-}$, and
$B^{\pm}\rightarrow\pi^{\pm}\pi^{+}\pi^{-}$ charmless three-body decays.
Significant CP asymmetries were found for the latter three B decay channels,
the last two being observed for the first time. The localized CP asymmetry
observed in specific regions of the phase space indicates that CP asymmetries
are unevenly distributed within it, with both positive and negative $A_{CP}$
values present in the same charged B decay channel.

The second analysis employs a novel method to measure CP asymmetries in
charmless $B\rightarrow PV$ decays, without the need for amplitude analyses,
considering two-body interaction with one spectator meson. It assumes constant
magnitudes and phases of amplitudes across the entire phase space, a common
assumption in amplitude analyses, supported by the quality of the fits. Five
decays are studied and for the $B^{\pm}\rightarrow\rho(770)^{0}K^{\pm}$ the CP
asymmetry obtained is $A_{CP}=+0.150\pm 0.019\pm 0.011$. For the remaining
channels, the observed CP asymmetries are compatible with zero.

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