mlsanityData to 14 Oct 2026

DCS-ST for Classification of Breast Cancer Histopathology Images with Limited Annotations

arXiv 2505.03204 ↗First posted 6 May 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

Deep learning methods have shown promise in classifying breast cancer histopathology images, but their performance often declines with limited annotated data, a critical challenge in medical imaging due to the high cost and expertise required for annotations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 44 references that are in the map. First posted on arXiv 6 May 2025.

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17 of 44 references.

References · 17

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  4. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  5. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  6. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,135
  7. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
  8. 2021CoAtNet: Marrying Convolution and Attention for All Data Sizescited by 569
  9. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  10. 2018BACH: Grand Challenge on Breast Cancer Histology Imagescited by 131
  11. 2023MedViT: A Robust Vision Transformer for Generalized Medical Image Classificationcited by 89
  12. 2021All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-trainingcited by 32
5 more
  1. 2019Context-Aware Self-Attention Networkscited by 25
  2. 2021Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretationcited by 17
  3. 2021Fast and Informative Model Selection using Learning Curve Cross-Validationcited by 15
  4. 2020Mini-DDSM: Mammography-based Automatic Age Estimationcited by 5
  5. 2024Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognitioncited by 2

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