Feature Extraction
PyTorch
English
moozy
pathology
computational-pathology
digital-pathology
foundation-model
whole-slide-image
vision-transformer
self-supervised-learning
slide-encoder
case-encoder
histopathology
medical-imaging
multiple-instance-learning
slide-level-representation
patient-level-representation
multi-task-learning
survival-analysis
cancer
oncology
tissue-classification
mutation-prediction
TCGA
CPTAC
transformer
Eval Results (legacy)
fix: list all 56 datasets explicitly in Tasks section
Browse files
README.md
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@@ -270,7 +270,7 @@ The output H5 file contains a `features` dataset (768-D float32 case embedding)
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## Tasks
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This repository includes 333 task definitions in the `tasks/` directory. Each task has a `config.yaml` (task type, organ, label mapping) and a `task.csv` (annotations and splits). The tasks cover 205 classification and 128 survival endpoints across 32 TCGA cohorts, 10 CPTAC cohorts,
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## Citation
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## Tasks
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This repository includes 333 task definitions in the `tasks/` directory. Each task has a `config.yaml` (task type, organ, label mapping) and a `task.csv` (annotations and splits). The tasks cover 205 classification and 128 survival endpoints across all 32 TCGA cohorts, all 10 CPTAC cohorts, REG, BC-Therapy, BRACS, CAMELYON17, DHMC Kidney, DHMC LUAD, EBRAINS, IMP Colorectum, IMP Cervix, MBC, MUT-HET-RCC, NADT Prostate, NAT-BRCA, and PANDA.
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## Citation
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