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.
arXiv preprint arXiv:2308.15474 , year=
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Retrieval-guided captioning from similar cases achieves higher semantic alignment (cosine similarity ~0.60 vs ~0.47) and fewer unsupported diagnoses than MedGemma on the ARCH dataset.
The paper surveys data-centric strategies for foundation models in computational healthcare and supplies a curated list of related models 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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Retrieval-Guided Generation for Safer Histopathology Image Captioning
Retrieval-guided captioning from similar cases achieves higher semantic alignment (cosine similarity ~0.60 vs ~0.47) and fewer unsupported diagnoses than MedGemma on the ARCH dataset.
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Data-Centric Foundation Models in Computational Healthcare: A Survey
The paper surveys data-centric strategies for foundation models in computational healthcare and supplies a curated list of related models and datasets.