HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images

• URL: http://arxiv.org/abs/2007.13007v1
• Date: Sat, 25 Jul 2020 20:42:21 GMT
• Title: HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images
• Authors: Sachin Mehta, Ximing Lu, Donald Weaver, Joann G. Elmore, Hannaneh Hajishirzi, Linda Shapiro
• Abstract summary: We introduce a novel attention-based network, the Holistic ATtention Network (HATNet) to classify breast biopsy images. It uses self-attention to encode global information, allowing it to learn representations from clinically relevant tissue structures without any explicit supervision. Our analysis reveals that HATNet learns representations from clinically relevant structures, and it matches the classification accuracy of human pathologists for this challenging test set.
• Score: 39.82731558467617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training end-to-end networks for classifying gigapixel size histopathological images is computationally intractable. Most approaches are patch-based and first learn local representations (patch-wise) before combining these local representations to produce image-level decisions. However, dividing large tissue structures into patches limits the context available to these networks, which may reduce their ability to learn representations from clinically relevant structures. In this paper, we introduce a novel attention-based network, the Holistic ATtention Network (HATNet) to classify breast biopsy images. We streamline the histopathological image classification pipeline and show how to learn representations from gigapixel size images end-to-end. HATNet extends the bag-of-words approach and uses self-attention to encode global information, allowing it to learn representations from clinically relevant tissue structures without any explicit supervision. It outperforms the previous best network Y-Net, which uses supervision in the form of tissue-level segmentation masks, by 8%. Importantly, our analysis reveals that HATNet learns representations from clinically relevant structures, and it matches the classification accuracy of human pathologists for this challenging test set. Our source code is available at \url{https://github.com/sacmehta/HATNet}

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