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Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification
March 14, 2024, 4:45 a.m. | Tingting Zheng, Kui Jiang, Hongxun Yao
cs.CV updates on arXiv.org arxiv.org
Abstract: Multi-Instance Learning (MIL) has shown impressive performance for histopathology whole slide image (WSI) analysis using bags or pseudo-bags. It involves instance sampling, feature representation, and decision-making. However, existing MIL-based technologies at least suffer from one or more of the following problems: 1) requiring high storage and intensive pre-processing for numerous instances (sampling); 2) potential over-fitting with limited knowledge to predict bag labels (feature representation); 3) pseudo-bag counts and prior biases affect model robustness and generalizability …
abstract analysis arxiv classification cs.cv decision dynamic feature however image instance least making mil performance policy representation sampling storage technologies type
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