ArticleJournal of imaging2024
Optimizing Vision Transformers for Histopathology: Pretraining and Normalization in Breast Cancer Classification.
Article in Journal of imaging, 2024. 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 Swin Transformer and Texture Feature Framework for Histopathological Classification of Paratuberculosis.Bioengineering (Basel, Switzerland) · 2026Article
- Reducing False Negatives in AI-Based Breast Histopathology: A Clinically Oriented Evaluation of Deep Learning Models Under Domain Shift.Diagnostics (Basel, Switzerland) · 2026Article
- HistoMap: Reconstructing Spatially Resolved Single-Cell Profiles from Bulk RNA-Seq to Decipher the Immune-Excluded Microenvironment in Colon Cancer.International journal of molecular sciences · 2026Article
- Enhanced Vision Transformer with Custom Attention Mechanism for Automated Idiopathic Scoliosis Classification.Journal of imaging informatics in medicine · 2026Article
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.Journal of imaging informatics in medicine · 2025Review
- Spectral-aware CNN with learnable biorthogonal units and depthwise convolutions for multi-class blood cell classification.MethodsX · 2025Article
- MFF-ClassificationNet: CNN-Transformer Hybrid with Multi-Feature Fusion for Breast Cancer Histopathology Classification.Biosensors · 2025Article
- Hierarchical Swin Transformer Ensemble with Explainable AI for Robust and Decentralized Breast Cancer Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- Exploring AI Approaches for Breast Cancer Detection and Diagnosis: A Review Article.Breast cancer (Dove Medical Press) · 2025Review
- Detection of Thymoma Disease Using mRMR Feature Selection and Transformer Models.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors.
Funding
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Abstract
This paper introduces a self-attention Vision Transformer model specifically developed for classifying breast cancer in histology images. We examine various training strategies and configurations, including pretraining, dimension resizing, data augmentation and color normalization strategies, patch overlap, and patch size configurations, in order to evaluate their impact on the effectiveness of the histology image classification. Additionally, we provide evidence for the increase in effectiveness gathered through geometric and color data augmentation techniques. We primarily utilize the BACH dataset to train and validate our methods and models, but we also test them on two additional datasets, BRACS and AIDPATH, to verify their generalization capabilities. Our model, developed from a transformer pretrained on ImageNet, achieves an accuracy rate of 0.91 on the BACH dataset, 0.74 on the BRACS dataset, and 0.92 on the AIDPATH dataset. Using a model based on the prostate small and prostate medium HistoEncoder models, we achieve accuracy rates of 0.89 and 0.86, respectively. Our results suggest that pretraining on large-scale general datasets like ImageNet is advantageous. We also show the potential benefits of using domain-specific pretraining datasets, such as extensive histopathological image collections as in HistoEncoder, though not yet with clear advantages.
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Registered trials
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