ArticleMedical image analysis2022
End-to-End diagnosis of breast biopsy images with transformers.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A hybrid ConvNeXt-ViT framework with differential evolution optimization for breast cancer classification.Scientific reports · 2026Article
- Sequential Transfer Learning for Multi-Domain Breast Image Segmentation Using a Transformer-Enhanced Hybrid U-Net.Bioengineering (Basel, Switzerland) · 2026Article
- TMAN: A Triple Morphological Feature Attention Network for Fine-Grained Classification of Breast Ultrasound Images.Journal of imaging informatics in medicine · 2026Article
- Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.Journal of imaging informatics in medicine · 2025Review
- Fusing global context with multiscale context for enhanced breast cancer classification.Scientific reports · 2024Article
- Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multi-modal data.Signal transduction and targeted therapy · 2024Article
- An Analytical Study on the Utility of RGB and Multispectral Imagery with Band Selection for Automated Tumor Grading.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- A semi-supervised segmentation method for microscopic hyperspectral pathological images based on multi-consistency learning.Frontiers in oncology · 2024Article
- A survey of Transformer applications for histopathological image analysis: New developments and future directions.Biomedical engineering online · 2023Review
- Automated analysis of whole slide digital skin biopsy images.Frontiers in artificial intelligence · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Diagnostic disagreements among pathologists occur throughout the spectrum of benign to malignant lesions. A computer-aided diagnostic system capable of reducing uncertainties would have important clinical impact. To develop a computer-aided diagnosis method for classifying breast biopsy images into a range of diagnostic categories (benign, atypia, ductal carcinoma in situ, and invasive breast cancer), we introduce a transformer-based hollistic attention network called HATNet. Unlike state-of-the-art histopathological image classification systems that use a two pronged approach, i.e., they first learn local representations using a multi-instance learning framework and then combine these local representations to produce image-level decisions, HATNet streamlines the histopathological image classification pipeline and shows 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 87 U.S. pathologists for this challenging test set.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.