Classification Of Breast Cancer Histology Images

4 papers with code • 0 benchmarks • 1 datasets

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Most implemented papers

Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks

bghojogh/Fisher-Triplet-Contrastive-Loss 5 Apr 2020

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.

Magnification Generalization for Histopathology Image Embedding

bghojogh/Histopathology-Magnification-Generalization 18 Jan 2021

However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level.

Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images

prakashchhipa/magnification-prior-self-supervised-method 15 Mar 2022

This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors.

BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

bupt-ai-cz/BCI 25 Apr 2022

The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer.