The Radiologist must analyze visually , based on his expertise , to determine whether it is cancer or not . We explore the use of CEP for breast cancer image classification and information enrichment . The results reveal that the application of event processing techniques improves the classification of images in the medical domain . The breast cancer histology image classification task is to classify images into four categories of normal , benign , in situ carcinoma , and invasive carcinoma . A common challenge of the breast cancer histology image classification , also in other medical domain , is a lack of sufficient data . To address the limitations of the small amount of data , we applied additional patch extraction ( APE ) of whole-slide image ( WSI ) as dataset extension approach . Automatic histopathology image recognition plays a key role in speeding up diagnosis and improving the quality of diagnosis . MethodsIn this work , we propose a breast cancer histopathology image classification by assembling multiple compact Convolutional Neural Networks ( CNNs ) . First , a hybrid CNN architecture is designed , which contains a global model branch and a local model branch . However , existing approaches showed less interest in exploring multi-layer features for improving the classification . We propose the integration of multi-layer features from a ResNet model for breast cancer histopathology image classification . Specifically , this work focuses on making a framework which considers both independent nature of layers as well as some partial dependency among them . The Earth Mover 's Distance ( EMD ) similarity metric and the K-Nearest Neighbors ( KNN ) classification algorithm provided correlations between the high-dimensional set of shape distributions and a priori known histological grades . Conclusion : Computational pattern analysis of histology shows promise as an effective software tool in breast cancer histological grading . For diagnosis , deep learning and more precisely Convolutional Neural Networks ( CNNs ) have shown a high potential in providing an automatic assistance to domain experts . This work explores and analyzes the performance of the state-of-the art CNN model in the problem of breast cancer histopathological images diagnosis . Pathological examination is the most accurate method for the diagnosis of cancer . Breast cancer histopathology evaluation analyses the chemical and cellular characteristics of the cells of a suspicious breast tumor . A computer-aided automatic classifier with the help of machine learning can improve the diagnosis system in terms of accuracy and time consumption . Convolution Neural Network has become a preferred choice for images analysis including breast histopathology . In this paper , we review various deep learning concepts applied to breast cancer histopathology analysis and summarizes contributions to this field . We present a summary of the recent developments and a discussion about the best practices done using deep in breast histopathology analysis and improvements that can be done in future research . Facing this problem , the development of automatic and precise diagnosis methods is challenging but also essential for the field . In this paper , we propose a new hybrid convolutional and recurrent deep neural network for breast cancer pathological image classification . Our method considers the short-term as well as the long-term spatial correlations between patches through RNN which is directly incorporated on top of a CNN feature extractor . Among the available screening methods , colonoscopy is most commonly used in the U.S. because of its capability of visualizing the entire colon and removing the polyps it detected . The current national guideline for colonoscopy screening recommends an observation-based screening strategy . Nevertheless , there is scant research studying the cost-effectiveness of the recommended observation-based strategy and its variants . Colonoscopy can identify CRC early as well as prevent the disease . The benefit of screening colonoscopy has been established , but the optimal frequency of follow-up colonoscopy is unknown and may vary based on findings from colonoscopy screening and patient age . Methods A partially observed Markov process ( POMP ) was used to simulate the effects of follow-up colonoscopy on the development of CRC . This aptasensor consists of a functionalized electrodes using various materials including 11-mercaptoandecanoic acid ( 11-MUA ) and modified KCHA10a aptamer . The HCT 116 , HT 29 and HEp-2 cell lines are used in this study to demonstrate the functionality of aptasensor for colon cancer detection purposes . Flow cytometry , fluorescence microscopy and electrochemical cyclic voltammetry are used to verify the binding between the target cells and aptamer . In the semi-supervised MPC , prior knowledge such as instance-level and attribute-level constraints are used to guide the clustering process towards better and interpretable partitions . We applied the unsupervised MPC ( null background ) to phylogenetic analysis of Mycobacterium and the semi-supervised MPC to colon cancer microarray data analysis . The results show that MPC is superior to the widely accepted neighbor-joining and hierarchical clustering methods , and the semi-supervised MPC is even more powerful in biological data analysis such as gene selection and cancer diagnosis using microarray . The challenge is the extraction of the real features that distinguish the benign and malignant tumors . In this paper , the extraction of the shape and texture characteristics of the images has been considered for classification of breast cancer images . The texture features are extracted using GLCM algorithm , while the shape features are extracted directly from the images . One of the approaches for dealing with data imbalance is artificially reducing the number of objects from the over-represented class , an approach called undersampling . In this paper we present a novel undersampling technique , Radial-Based Undersampling ( RBU ) , and apply it to the classification of breast cancer histopathological images . The results of the conducted experimental analysis not only indicate the usefulness of the RBU algorithm in that task , but also shed light on some of the drawbacks of that method , which could potentially be used in the future to improve upon the proposed algorithm . Classifying breast cancer histopathological images automatically is an important task in computer assisted pathology analysis . However , extracting informative and non-redundant features for histopathological image classification is challenging due to the appearance variability caused by the heterogeneity of the disease , the tissue preparation , and staining processes . With the recent advances in image processing and machine learning , there is an interest in attempting to develop a reliable pattern recognition based systems to improve the quality of diagnosis . In this paper , we compare two machine learning approaches for the automatic classification of breast cancer histology images into benign and malignant and into benign and malignant sub-classes . The first approach is based on the extraction of a set of handcrafted features encoded by two coding models ( bag of words and locality constrained linear coding ) and trained by support vector machines , while the second approach is based on the design of convolutional neural networks . In addition , we propose a new learning rate scheduler which can get excellent performance without complicatedly fine-tuning the learning rate . We use our model for the automatic classification of breast cancer histology images ( BreakHis dataset ) into benign and malignant and eight subtypes . The results show that our model achieves the accuracy between 98.87 % and 99.34 % for the binary classification and achieve the accuracy between 90.66 % and 93.81 % for the multi-class classification . The automatic classification of breast cancer histopathological images has great significance in computer-aided diagnosis . Recently , deep learning via neural networks has enabled pattern detection and prediction using large , labeled datasets ; whereas , collecting and annotating sufficient histological data using professional pathologists is time consuming , tedious , and extremely expensive . We propose to overcome such limitations with a two-stage semi-supervised approach that combines the power of data-efficient self-supervised feature learning via contrastive predictive coding ( CPC ) and the interpretability and flexibility of regularized attention-based MIL . We apply our two-stage CPC + MIL semi-supervised pipeline to the binary classification of breast cancer histology images . Across five random splits , we report state-of-the-art performance with a mean validation accuracy of 95 % and an area under the ROC curve of 0.968 . Although many researches focused on cancer grade prediction , hardly most of them attempted to integrate multiple data types , by which we can not only improve and boost results obtained from learning method , but also have a good understanding or explanation of biological issues . In this paper , we take advantage of a sophisticated supervised learning method called multiple kernel learning ( MKL ) to design a breast cancer grading predictor fusing heterogeneous data for classification of breast cancer histopathology . Furthermore , we modify our model by involving biological pathway information . Pathologists must be highly focused on the diagnostic process , but it is still prone to missed diagnosis . This paper presents a complete diagnostic system for the classification of pathological images of metastatic breast cancer , which can automatically identify metastatic breast cancer cells . In this paper , we use convolutional neural networks to extract images intensively and add attention mechanisms to score and re-learn the features to ensure that the effective features of metastatic cancer can be learned quickly and effectively . Cancer investigations in microarray data play a major role in cancer analysis and the treatment . Cancer microarray data consists of complex gene expressed patterns of cancer . The paper presents the historical development ofneutron beams used for BNCT , addresses the requirementson the types of beams , describes some ofthe existing designs and other proposals elsewhere andlastly , considers the broader requirements in designing NCTfacilities . The focus of the paper is ontreatment of brain cancer , neutron beam requirements forother types of cancer may vary . However , the CHAID had lower specificity ( 77.83 % ) than logistic regression ( 88.70 % ) . In addition , we demonstrated how the decision tree algorithm could be used in risk analysis and target segmentation for cervical cancer management . This is the first study using induction technique for the analysis of risk factors for cervical cancer , and the results of this study will contribute to developing the clinical practice guideline for cervical cancer . Once mutations are detected in any of the biomarker genes found in a given patient , such patient is recommended for targeted therapy to promptly curtail the progression of the mutations to advanced stages . The breathogenomics architecture serves as a generic template for the development of clinical equipment for breath and genomic based screening , early diagnosis and genotyping of lung cancer . In this paper , we report the preliminary result obtained from the prototype that we are currently developing based on the architecture . Our study reveals a number of design opportunities for supporting navigation in the areas of resource monitoring , knowledge transfer , case management , long term navigation , and development of best practices . Supporting cancer navigation will be a critical step towards improving the healthcare experience for cancer patients . Purpose . The objective of our study was to predicate candidate genes in cervical cancer ( CC ) using a network-based strategy and to understand the pathogenic process of CC . Methods . Pancreatic cancer acts as one of the leading causes of cancer-connective deaths . Its five-year overall survival rate being reported is about 7.7 % from 2006 to 2012 by the National Cancer Institute . Cancer maps are important tools in the descriptive presentation of the cancer burden . The objective is to explore the advantages and disadvantages of mapping methods based on point data in comparison with maps based on aggregated data . Early diagnosis of malignant melanoma is a crucial issue among the experts dermatologist . Rather than clinical examination there are many new imaging technologies such as dermoscopy , infrared imaging , multispectral imaging , and confocal microscopy can serve as an adjunct to physicians and provide automated skin cancer screening . Computer Vision Diagnosis system can be very helpful in detecting and diagnosing skin pigmentation earlier and faster than typical screening programs . The functions of the system include the quantification of the DNA content , the analysis of DNA ploidy as well as cell morphologic analysis . It can offer reliable data for the pathologic diagnosis of cancer cell . The hardware and software design of the system is presented . To demonstrate the performance of the RU - Net and R2U - Net models , we have tested them on three different medical imaging datasets . These include blood vessel segmentations from retina images ( DRIVE , STARE , and CHASE_DB1 shown in Fig . 6 ) , skin cancer lesion segmentation , and lung segmentation from 2D images . For this implementation , the Keras , and TensorFlow frameworks are used on a single GPU machine with 56 G of RAM and an NIVIDIA GEFORCE GTX - 980
The Radiologist must analyze visually , based on his expertise , to determine whether it is cancer or not . We explore the use of CEP for breast cancer image classification and information enrichment . The results reveal that the application of event processing techniques improves the classification of images in the medical domain .
The breast cancer histology image classification task is to classify images into four categories of normal , benign , in situ carcinoma , and invasive carcinoma . A common challenge of the breast cancer histology image classification , also in other medical domain , is a lack of sufficient data . To address the limitations of the small amount of data , we applied additional patch extraction ( APE ) of whole-slide image ( WSI ) as dataset extension approach .
Automatic histopathology image recognition plays a key role in speeding up diagnosis and improving the quality of diagnosis . MethodsIn this work , we propose a breast cancer histopathology image classification by assembling multiple compact Convolutional Neural Networks ( CNNs ) . First , a hybrid CNN architecture is designed , which contains a global model branch and a local model branch .
However , existing approaches showed less interest in exploring multi-layer features for improving the classification . We propose the integration of multi-layer features from a ResNet model for breast cancer histopathology image classification . Specifically , this work focuses on making a framework which considers both independent nature of layers as well as some partial dependency among them .
The Earth Mover 's Distance ( EMD ) similarity metric and the K-Nearest Neighbors ( KNN ) classification algorithm provided correlations between the high-dimensional set of shape distributions and a priori known histological grades . Conclusion : Computational pattern analysis of histology shows promise as an effective software tool in breast cancer histological grading .
For diagnosis , deep learning and more precisely Convolutional Neural Networks ( CNNs ) have shown a high potential in providing an automatic assistance to domain experts . This work explores and analyzes the performance of the state-of-the art CNN model in the problem of breast cancer histopathological images diagnosis .
Pathological examination is the most accurate method for the diagnosis of cancer . Breast cancer histopathology evaluation analyses the chemical and cellular characteristics of the cells of a suspicious breast tumor . A computer-aided automatic classifier with the help of machine learning can improve the diagnosis system in terms of accuracy and time consumption .
Convolution Neural Network has become a preferred choice for images analysis including breast histopathology . In this paper , we review various deep learning concepts applied to breast cancer histopathology analysis and summarizes contributions to this field . We present a summary of the recent developments and a discussion about the best practices done using deep in breast histopathology analysis and improvements that can be done in future research .
Facing this problem , the development of automatic and precise diagnosis methods is challenging but also essential for the field . In this paper , we propose a new hybrid convolutional and recurrent deep neural network for breast cancer pathological image classification . Our method considers the short-term as well as the long-term spatial correlations between patches through RNN which is directly incorporated on top of a CNN feature extractor .
Among the available screening methods , colonoscopy is most commonly used in the U.S. because of its capability of visualizing the entire colon and removing the polyps it detected . The current national guideline for colonoscopy screening recommends an observation-based screening strategy . Nevertheless , there is scant research studying the cost-effectiveness of the recommended observation-based strategy and its variants .
Colonoscopy can identify CRC early as well as prevent the disease . The benefit of screening colonoscopy has been established , but the optimal frequency of follow-up colonoscopy is unknown and may vary based on findings from colonoscopy screening and patient age . Methods A partially observed Markov process ( POMP ) was used to simulate the effects of follow-up colonoscopy on the development of CRC .
This aptasensor consists of a functionalized electrodes using various materials including 11-mercaptoandecanoic acid ( 11-MUA ) and modified KCHA10a aptamer . The HCT 116 , HT 29 and HEp-2 cell lines are used in this study to demonstrate the functionality of aptasensor for colon cancer detection purposes . Flow cytometry , fluorescence microscopy and electrochemical cyclic voltammetry are used to verify the binding between the target cells and aptamer .
In the semi-supervised MPC , prior knowledge such as instance-level and attribute-level constraints are used to guide the clustering process towards better and interpretable partitions . We applied the unsupervised MPC ( null background ) to phylogenetic analysis of Mycobacterium and the semi-supervised MPC to colon cancer microarray data analysis . The results show that MPC is superior to the widely accepted neighbor-joining and hierarchical clustering methods , and the semi-supervised MPC is even more powerful in biological data analysis such as gene selection and cancer diagnosis using microarray .
The challenge is the extraction of the real features that distinguish the benign and malignant tumors . In this paper , the extraction of the shape and texture characteristics of the images has been considered for classification of breast cancer images . The texture features are extracted using GLCM algorithm , while the shape features are extracted directly from the images .
One of the approaches for dealing with data imbalance is artificially reducing the number of objects from the over-represented class , an approach called undersampling . In this paper we present a novel undersampling technique , Radial-Based Undersampling ( RBU ) , and apply it to the classification of breast cancer histopathological images . The results of the conducted experimental analysis not only indicate the usefulness of the RBU algorithm in that task , but also shed light on some of the drawbacks of that method , which could potentially be used in the future to improve upon the proposed algorithm .
Classifying breast cancer histopathological images automatically is an important task in computer assisted pathology analysis . However , extracting informative and non-redundant features for histopathological image classification is challenging due to the appearance variability caused by the heterogeneity of the disease , the tissue preparation , and staining processes .
With the recent advances in image processing and machine learning , there is an interest in attempting to develop a reliable pattern recognition based systems to improve the quality of diagnosis . In this paper , we compare two machine learning approaches for the automatic classification of breast cancer histology images into benign and malignant and into benign and malignant sub-classes . The first approach is based on the extraction of a set of handcrafted features encoded by two coding models ( bag of words and locality constrained linear coding ) and trained by support vector machines , while the second approach is based on the design of convolutional neural networks .
In addition , we propose a new learning rate scheduler which can get excellent performance without complicatedly fine-tuning the learning rate . We use our model for the automatic classification of breast cancer histology images ( BreakHis dataset ) into benign and malignant and eight subtypes . The results show that our model achieves the accuracy between 98.87 % and 99.34 % for the binary classification and achieve the accuracy between 90.66 % and 93.81 % for the multi-class classification .
The automatic classification of breast cancer histopathological images has great significance in computer-aided diagnosis . Recently , deep learning via neural networks has enabled pattern detection and prediction using large , labeled datasets ; whereas , collecting and annotating sufficient histological data using professional pathologists is time consuming , tedious , and extremely expensive .
We propose to overcome such limitations with a two-stage semi-supervised approach that combines the power of data-efficient self-supervised feature learning via contrastive predictive coding ( CPC ) and the interpretability and flexibility of regularized attention-based MIL . We apply our two-stage CPC + MIL semi-supervised pipeline to the binary classification of breast cancer histology images . Across five random splits , we report state-of-the-art performance with a mean validation accuracy of 95 % and an area under the ROC curve of 0.968 .
Although many researches focused on cancer grade prediction , hardly most of them attempted to integrate multiple data types , by which we can not only improve and boost results obtained from learning method , but also have a good understanding or explanation of biological issues . In this paper , we take advantage of a sophisticated supervised learning method called multiple kernel learning ( MKL ) to design a breast cancer grading predictor fusing heterogeneous data for classification of breast cancer histopathology . Furthermore , we modify our model by involving biological pathway information .
Pathologists must be highly focused on the diagnostic process , but it is still prone to missed diagnosis . This paper presents a complete diagnostic system for the classification of pathological images of metastatic breast cancer , which can automatically identify metastatic breast cancer cells . In this paper , we use convolutional neural networks to extract images intensively and add attention mechanisms to score and re-learn the features to ensure that the effective features of metastatic cancer can be learned quickly and effectively .
Cancer investigations in microarray data play a major role in cancer analysis and the treatment . Cancer microarray data consists of complex gene expressed patterns of cancer .
The paper presents the historical development ofneutron beams used for BNCT , addresses the requirementson the types of beams , describes some ofthe existing designs and other proposals elsewhere andlastly , considers the broader requirements in designing NCTfacilities . The focus of the paper is ontreatment of brain cancer , neutron beam requirements forother types of cancer may vary .
However , the CHAID had lower specificity ( 77.83 % ) than logistic regression ( 88.70 % ) . In addition , we demonstrated how the decision tree algorithm could be used in risk analysis and target segmentation for cervical cancer management . This is the first study using induction technique for the analysis of risk factors for cervical cancer , and the results of this study will contribute to developing the clinical practice guideline for cervical cancer .
Once mutations are detected in any of the biomarker genes found in a given patient , such patient is recommended for targeted therapy to promptly curtail the progression of the mutations to advanced stages . The breathogenomics architecture serves as a generic template for the development of clinical equipment for breath and genomic based screening , early diagnosis and genotyping of lung cancer . In this paper , we report the preliminary result obtained from the prototype that we are currently developing based on the architecture .
Our study reveals a number of design opportunities for supporting navigation in the areas of resource monitoring , knowledge transfer , case management , long term navigation , and development of best practices . Supporting cancer navigation will be a critical step towards improving the healthcare experience for cancer patients .
Purpose . The objective of our study was to predicate candidate genes in cervical cancer ( CC ) using a network-based strategy and to understand the pathogenic process of CC . Methods .
Pancreatic cancer acts as one of the leading causes of cancer-connective deaths . Its five-year overall survival rate being reported is about 7.7 % from 2006 to 2012 by the National Cancer Institute .
Cancer maps are important tools in the descriptive presentation of the cancer burden . The objective is to explore the advantages and disadvantages of mapping methods based on point data in comparison with maps based on aggregated data .
Early diagnosis of malignant melanoma is a crucial issue among the experts dermatologist . Rather than clinical examination there are many new imaging technologies such as dermoscopy , infrared imaging , multispectral imaging , and confocal microscopy can serve as an adjunct to physicians and provide automated skin cancer screening . Computer Vision Diagnosis system can be very helpful in detecting and diagnosing skin pigmentation earlier and faster than typical screening programs .
The functions of the system include the quantification of the DNA content , the analysis of DNA ploidy as well as cell morphologic analysis . It can offer reliable data for the pathologic diagnosis of cancer cell . The hardware and software design of the system is presented .
To demonstrate the performance of the RU - Net and R2U - Net models , we have tested them on three different medical imaging datasets . These include blood vessel segmentations from retina images ( DRIVE , STARE , and CHASE_DB1 shown in Fig . 6 ) , skin cancer lesion segmentation , and lung segmentation from 2D images . For this implementation , the Keras , and TensorFlow frameworks are used on a single GPU machine with 56 G of RAM and an NIVIDIA GEFORCE GTX - 980
Breast cancer image classification based on a Complex Event Processing engine Breast Cancer Histology Image Classification using Deep Learning Breast cancer histopathology image classification through assembling multiple compact CNNs Partially-Independent Framework for Breast Cancer Histopathological Image Classification Image-based histologic grade estimation using stochastic geometry analysis A first study exploring the performance of the state-of-the art CNN model in the problem of breast cancer A Study on Deep Learning for Breast Cancer Detection in Histopathological Images A Hybrid Convolutional and Recurrent Deep Neural Network for Breast Cancer Pathological Image Classification Using a partially observable Markov chain model to assess colonoscopy screening strategies - A cohort study Evaluating the Cost-Effective Use of Follow-Up Colonoscopy Based on Screening Findings and Age An Apta-Biosensor for Colon Cancer Diagnostics On clustering biological data using unsupervised and semi-supervised message passing ISIBC: An intelligent system for identification of breast cancer Radial-Based Undersampling Algorithm for Classification of Breast Cancer Histopathological Images Affected by Data Imbalance Deep Manifold Preserving Autoencoder for Classifying Breast Cancer Histopathological Images Classification of Breast Cancer Based on Histology Images Using Convolutional Neural Networks Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module Label-Efficient Breast Cancer Histopathological Image Classification Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding The method for breast cancer grade prediction and pathway analysis based on improved multiple kernel learning Se-DenseNet: Attention-Based Network for Detecting Pathological Images of Metastatic Breast Cancer Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm The requirements and development of neutron beams for neutron capture therapy of brain cancer Analysis on risk factors for cervical cancer using induction technique Breathogenomics: A Computational Architecture for Screening, Early Diagnosis and Genotyping of Lung Cancer Cancer navigation: opportunities and challenges for facilitating the breast cancer journey Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer Pancreatic cancer biomarker detection using recursive feature elimination based on Support Vector Machine and large margin distribution machine Local standardized incidence ratio estimates and comparison with other mapping methods for small geographical areas using Slovenian breast cancer data Cad System for Dermoscopy Images The Development of a Cancer Cell DNA Image Analysis System Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
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ISCIT CVPR Workshops Medical Imaging: Computer-Aided Diagnosis LOPAL CVIP (2) BIBM BIBE ICCPR IWBBIO (2) CSCW ICSAI ArXiv
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