Classification Of Breast Cancer Histology Images

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Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (11 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning Method
  6 May 2024 1 repository listed
  Deep neural networks have reached remarkable achievements in medical image processing tasks, specifically in classifying and detecting various diseases.
• BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix
  25 Apr 2022 1 repository listed
  The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer.
• Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images
  15 Mar 2022 1 repository listed
  This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors.
• Magnification Generalization for Histopathology Image Embedding
  18 Jan 2021 1 repository listed
  However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level.
• Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
  5 Apr 2020 1 repository listed
  The FDT and FDC loss functions are designed based on the statistical formulation of the Fisher Discriminant Analysis (FDA), which is a linear subspace learning method.