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Found 2 papers, 2 papers with code
Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification
Whole slide image (WSI) classification is a fundamental task for the diagnosis and treatment of diseases; but, curation of accurate labels is time-consuming and limits the application of fully-supervised methods.
Multiple Instance Learning with Center Embeddings for Histopathology Classification
To address this, recent methods have considered WSI classification as a Multiple Instance Learning (MIL) problem often with a multi-stage process for learning instance and slide level features.
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