The Self-Supervision Regime and Encoder Fit for Histopathology Image Analysis#1132
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The Self-Supervision Regime and Encoder Fit for Histopathology Image Analysis#1132crovo77 wants to merge 1 commit intosunlabuiuc:masterfrom
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Contributors: Armaan Mehta (armaanm3@illinois.edu), Stanley Su (ssu603@illinois.edu), Carson Crovo (ccrovo2@illinois.edu)
Contribution Type: New Dataset, New Task, New Model
Description: Added support for the TCGA-CRCk dataset and an associated task (groups tiles from Whole Slide Images into patient-level samples). Added model for self-attention Multiple Instance Learning which uses SimCLR encodings obtained through a replication of "The Self-Supervision Regime and Encoder Fit for Histopathology Image Analysis" (https://proceedings.mlr.press/v259/azhar25a.html).
Files to Review:
pyhealth/datasets/tcga_crck.py- Main dataset implementationpyhealth/datasets/configs/tcga_crck.yamlpyhealth/tasks/tcga_crck_msi_classification.py- Associated taskpyhealth/models/tcga_crck_simclr_mil.py- Associated modeltests/core/test_tcga_crck.py- Test Cases for dataset and tasktests/core/test_tcga_crck_simclr_mil.py- Test Cases for modelexamples/tcga_crck_simclr/tcga_crck_msi_classification_tissue_aware_simclr.py- Example full pipelinedocs/api/datasets/pyhealth.datasets.TCGACRCkDataset.rstdocs/api/tasks/pyhealth.tasks.TCGACRCkMSIClassification.rstdocs/api/models/pyhealth.models.TissueAwareSimCLR.rst