Breast Cancer Microscope Image Classification Based on CNN with Image Deformation

Wang, Yaqi; Sun, Lingling; Ma, Kebo; Fang, Jiannan

doi:10.1007/978-3-319-93000-8_96

“…Each configuration was trained on the images from Part A and the classification of a test image is obtained by computing the highest class probability provided by the three configurations. (Wang et al, 2018a) proposed the direct application of VGG-16 (Simonyan and Zisserman, 2014) to solve Part A. Prior to fine-tuning the model, all images from Part A were resized to 256×256 and normalized to zero mean and unit standard deviation.…”

Section: Brancati Et Al (Team 1)mentioning

confidence: 99%

BACH: Grand challenge on breast cancer histology images

Aresta

¹

,

Araújo

²

,

Kwok³

et al. 2019

Medical Image Analysis

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A B S T R A C TBreast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time-and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology.

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“…In [104], a method for classification of breast cancer histopathology images based on deep learning is proposed. The effects of various preprocessing methods are compared, and the classification results of CNN and CNN with SVM are also compared.…”

Section: ) ''Bach'' Tasksmentioning

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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“…In [105], a method for classification of breast cancer histopathology images based on deep learning is proposed. The effects of various preprocessing methods are compared, and the classification results of CNN and CNN with SVM are also compared.…”

Section: Related Work Of Breakhis In 2020mentioning

confidence: 99%

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

Zhou,

Li,

Rahaman

et al. 2020

Preprint

0

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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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Breast Cancer Microscope Image Classification Based on CNN with Image Deformation

Cited by 29 publications

References 3 publications

BACH: Grand challenge on breast cancer histology images

BACH: Grand challenge on breast cancer histology images

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

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