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Found 3 papers, 3 papers with code
Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary
Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined.
Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning
To validate that these model explanations are prognostic, we further analyzed high attention morphological regions in WSIs, which indicates that tumor-infiltrating lymphocyte presence corroborates with favorable cancer prognosis on 9 out of 14 cancer types studied.
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images
Survival outcome prediction is a challenging weakly-supervised and ordinal regression task in computational pathology that involves modeling complex interactions within the tumor microenvironment in gigapixel whole slide images (WSIs).
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