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Diagnose Like Doctors: Weakly Supervised Fine-Grained Classification of Breast Cancer

Published: 16 February 2023 Publication History

Abstract

Breast cancer is the most common type of cancers in women. Therefore, how to accurately and timely diagnose it becomes very important. Some computer-aided diagnosis models based on pathological images have been proposed for this task. However, there are still some issues that need to be further addressed. For example, most deep learning based models suffer from a lack of interpretability. In addition, some of them cannot fully exploit the information in medical data, e.g., hierarchical label structure and scattered distribution of target objects. To address these issues, we propose a weakly supervised fine-grained medical image classification method for breast cancer diagnosis, i.e., DLD-Net for short. It simulates the diagnostic procedures of pathologists by multiple attention-guided cropping and dropping operations, making it have good clinical interpretability. Moreover, it cannot only exploit the global information of a whole image, but also further mine the critical local information by generating and selecting critical regions from the image. In light of this, those subtle discriminating information hidden in scattered regions can be exploited. In addition, we also design a novel hierarchical cross-entropy loss to utilize the hierarchical label information in medical images, making the classification results more discriminative. Furthermore, DLD-Net is a weakly supervised network, which can be trained end-to-end without any additional region annotations. Extensive experimental results on three benchmark datasets demonstrate that DLD-Net is able to achieve good results and outperforms some state-of-the-art methods.

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Cited By

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  • (2024)A Hierarchically Discriminative Loss with Group Regularization for Fine-Grained Image ClassificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3698398Online publication date: 7-Oct-2024
  • (2024)Publicly available datasets of breast histopathology H&E whole-slide images: A scoping reviewJournal of Pathology Informatics10.1016/j.jpi.2024.10036315(100363)Online publication date: Dec-2024
  • (2024)Rethinking confidence scores for source-free unsupervised domain adaptationNeural Computing and Applications10.1007/s00521-024-09867-936:24(14951-14966)Online publication date: 1-Aug-2024

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Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology  Volume 14, Issue 2
April 2023
430 pages
ISSN:2157-6904
EISSN:2157-6912
DOI:10.1145/3582879
  • Editor:
  • Huan Liu
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 February 2023
Online AM: 23 November 2022
Accepted: 16 November 2022
Revised: 03 November 2022
Received: 16 December 2021
Published in TIST Volume 14, Issue 2

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Author Tags

  1. Breast cancer classification
  2. Diagnose Like Doctors
  3. Weakly-supervised network
  4. fine-grained classification

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  • National Natural Science Foundation of China

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Cited By

View all
  • (2024)A Hierarchically Discriminative Loss with Group Regularization for Fine-Grained Image ClassificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3698398Online publication date: 7-Oct-2024
  • (2024)Publicly available datasets of breast histopathology H&E whole-slide images: A scoping reviewJournal of Pathology Informatics10.1016/j.jpi.2024.10036315(100363)Online publication date: Dec-2024
  • (2024)Rethinking confidence scores for source-free unsupervised domain adaptationNeural Computing and Applications10.1007/s00521-024-09867-936:24(14951-14966)Online publication date: 1-Aug-2024
  • (2023)A plug-and-play noise-label correction framework for unsupervised domain adaptation person re-identificationThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-03094-440:6(4493-4504)Online publication date: 24-Sep-2023

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