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.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
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A Gaussian mixture MIL framework with partially subsampled instances improves metastasis prediction accuracy on breast cancer whole-slide images over prior methods.
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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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Detecting Breast Carcinoma Metastasis on Whole-Slide Images by Partially Subsampled Multiple Instance Learning
A Gaussian mixture MIL framework with partially subsampled instances improves metastasis prediction accuracy on breast cancer whole-slide images over prior methods.