GCE-MIL is a backbone-agnostic wrapper that directly optimizes MIL evidence for sufficiency, necessity, and recoverability, yielding modest gains in Macro-F1 and C-index plus more faithful patch selection across many backbones and datasets.
International conference on medical image computing and computer-assisted intervention , pages=
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Standard MIL models for whole-slide pathology images exhibit spatial blindness under coordinate permutation; ResTopoMIL separates appearance and spatial learning to restore sensitivity and improve classification and survival prediction.
Multi-Beholder integrates one-class classification into multiple instance learning to predict LGG biomarker status from histopathology images, reporting AUCs of 0.973 on TCGA-LGG and 0.820 on an external Xiangya cohort.
citing papers explorer
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GCE-MIL: Faithful and Recoverable Evidence for Multiple Instance Learning in Whole-Slide Imaging
GCE-MIL is a backbone-agnostic wrapper that directly optimizes MIL evidence for sufficiency, necessity, and recoverability, yielding modest gains in Macro-F1 and C-index plus more faithful patch selection across many backbones and datasets.
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Spatial Blindness in Whole-Slide Multiple Instance Learning
Standard MIL models for whole-slide pathology images exhibit spatial blindness under coordinate permutation; ResTopoMIL separates appearance and spatial learning to restore sensitivity and improve classification and survival prediction.
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Multi-Beholder: Biomarker Prediction for Low-Grade Glioma with Multiple Instance Learning and One-Class Classification
Multi-Beholder integrates one-class classification into multiple instance learning to predict LGG biomarker status from histopathology images, reporting AUCs of 0.973 on TCGA-LGG and 0.820 on an external Xiangya cohort.