Classification Of Breast Cancer Histology Images
4 papers with code • 0 benchmarks • 1 datasets
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BCI
Most implemented papers
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
bghojogh/Fisher-Triplet-Contrastive-Loss
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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
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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
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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
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The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer.
