Histopathological image classification using random binary hashing based PCANet and bilinear classifier

Wu, Jinjie; Li, Yan; Suo, Jingfeng; Zhang, Qi

doi:10.1109/eusipco.2016.7760609

“…In [132], a Principal Component Analysis Network (PCANet) is introduced to classify Ductal Carcinoma In-Situ (DCIS) and Usual Ductal Hyperplasia (UDH) images. In this work, a dataset with 20 DCIS and 31 UDH images are tested, where 10,000 patches are randomly sampled from the training set to learn the models.…”

Section: ) Other Tasks A: Classificationmentioning

confidence: 99%

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Xiao-min

¹

,

Li

²

,

Rahaman

³

et al. 2020

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Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.

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“…As a result, an F-measure of 71.80% and an accuracy of 84.23% are obtained for automatic detection of IDC regions in WSI. In [132], a Principal Component Analysis Network (PCANet) is introduced to classify Ductal Carcinoma In-Situ (DCIS) and Usual Ductal Hyperplasia (UDH) images. In this work, a dataset with 20 DCIS and 31 UDH images are tested, where 10,000 patches are randomly sampled from the training set to learn the models.…”

Section: "Idc" Tasksmentioning

confidence: 99%

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Zhou,

Li,

Rahaman

et al. 2020

Preprint

0

View full text Add to dashboard Cite

Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.

show abstract

A Survey for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Li

¹

,

Dong

²

,

Hu

³

et al. 2019

Advances in Intelligent Systems and Computing

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Histopathological image classification using random binary hashing based PCANet and bilinear classifier

Cited by 11 publications

References 11 publications

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

A Survey for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

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