Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

By Alexander Rakhlin, Alexey A. Shvets, Vladimir I. Iglovikov, Alexandr A Kalinin. 2018

343 citations recorded by the source corpus.

Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class classification task, we report 87.2% accuracy. For 2-class classification task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image classification. The source code for our approach is made publicly available at https://github.com/alexander-rakhlin/ICIAR2018

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks (2018, methodology): “In the domain of medical imaging, convolutional neural networks (CNN) have been successfully used, for example, for breast cancer histology image analysis [17], bone disease prediction [18] and age assessment [19], and other problems [15].”
3D Cell Nuclear Morphology: Microscopy Imaging Dataset And Voxel-Based Morphometry Classification Results (2017, background): “Recent examples in medical image analysis have already demonstrated successful applications of such models in the small data regime [13, 26].”
Breast cancer histopathological image classification using a hybrid deep neural network. (2020, methodology): “Rakhlin et al. [23] proposed a different method named deep convolutional feature re-presentation.”
Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks (2017, methodology): “In the domain of medical imaging, convolutional neural networks (CNN) have been successfully used, for example, for diabetic retinopathy screening [15], for breast cancer histology image analysis [16], bone disease prediction [25], and other problems [1].”
Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning (2018, methodology): “In the domain of medical imaging, convolutional neural networks (CNN) have been successfully used, for example, for breast cancer histology image analysis [17], bone disease prediction [21] and age assessment [10], and other problems [5].”
Camera Model Identification Using Convolutional Neural Networks (2018, background): “Problems in satellite [1], [2], [3], [4], medical [5], [6], [7], [8] or any other imagery are successfully tackled with these techniques, routinely beating human performance in many tasks, including classiﬁcation, segmentation, and detection.”
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, methodology): “Finally, all the patch predictions were averaged by Rakhlin et al. [94] and the image-level class was defined using the maximum probability score.”