ArticleFrontiers in pharmacology2022
Semi-supervised vision transformer with adaptive token sampling for breast cancer classification.
Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 63 citations in OpenAlex.
- Multi-OCT-SelfNet: integrating self-supervised learning with multi-source data fusion for enhanced multi-class retinal disease classification.Frontiers in systems biology · 2026Article
- MViT: A vision transformer with fractal path reordering and dynamic positional encoding.PloS one · 2026Article
- Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.Journal of imaging informatics in medicine · 2025Review
- Leveraging an ensemble of EfficientNetV1 and EfficientNetV2 models for classification and interpretation of breast cancer histopathology images.Scientific reports · 2025Article
- Class-aware multi-level attention learning for semi-supervised breast cancer diagnosis under imbalanced label distribution.Medical & biological engineering & computing · 2025Article
- AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology.NPJ digital medicine · 2025Article
- MammoViT: A Custom Vision Transformer Architecture for Accurate BIRADS Classification in Mammogram Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Fusion feature-based hybrid methods for diagnosing oral squamous cell carcinoma in histopathological images.Frontiers in oncology · 2025Article
- OCT-SelfNet: a self-supervised framework with multi-source datasets for generalized retinal disease detection.Frontiers in big data · 2025Article
- Self-supervised learning and transformer-based technologies in breast cancer imaging.Frontiers in radiology · 2025Review
- Segmentation for mammography classification utilizing deep convolutional neural network.BMC medical imaging · 2024Article
- BCED-Net: Breast Cancer Ensemble Diagnosis Network using transfer learning and the XGBoost classifier with mammography images.Osong public health and research perspectives · 2024Article
- Transformer models in biomedicine.BMC medical informatics and decision making · 2024Review
- Optimizing Vision Transformers for Histopathology: Pretraining and Normalization in Breast Cancer Classification.Journal of imaging · 2024Article
- Ultrasound Image Analysis with Vision Transformers-Review.Diagnostics (Basel, Switzerland) · 2024Review
- GSDA: Generative adversarial network-based semi-supervised data augmentation for ultrasound image classification.Heliyon · 2023Article
- Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of TInternational journal of molecular sciences · 2023Article
- ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images.Diagnostics (Basel, Switzerland) · 2022Article
- A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms.Biomedicines · 2022Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Various imaging techniques combined with machine learning (ML) models have been used to build computer-aided diagnosis (CAD) systems for breast cancer (BC) detection and classification. The rise of deep learning models in recent years, represented by convolutional neural network (CNN) models, has pushed the accuracy of ML-based CAD systems to a new level that is comparable to human experts. Existing studies have explored the usage of a wide spectrum of CNN models for BC detection, and supervised learning has been the mainstream. In this study, we propose a semi-supervised learning framework based on the Vision Transformer (ViT). The ViT is a model that has been validated to outperform CNN models on numerous classification benchmarks but its application in BC detection has been rare. The proposed method offers a custom semi-supervised learning procedure that unifies both supervised and consistency training to enhance the robustness of the model. In addition, the method uses an adaptive token sampling technique that can strategically sample the most significant tokens from the input image, leading to an effective performance gain. We validate our method on two datasets with ultrasound and histopathology images. Results demonstrate that our method can consistently outperform the CNN baselines for both learning tasks. The code repository of the project is available at https://github.com/FeiYee/Breast-area-TWO.
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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.