DECAT classifies multimodal representations into four diagnostic scenarios using null-referenced metrics and a rule-based procedure to detect shared biology versus confounders without knowing the confounder identity.
Training state-of-the-art pathology foundation models with orders of magnitude less data
3 Pith papers cite this work. Polarity classification is still indexing.
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PC-MIL shows that anchoring supervision at a 2 mm scale and progressively mixing slide- and region-level labels improves cross-context accuracy in WSI cancer detection without reducing global performance.
A masked-diffusion pretrained convolutional model outperforms ViT pathology foundation models on cell-level dense prediction tasks in histology.
citing papers explorer
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When Are Multimodal Predictions Biologically Supported? A Diagnostic Evaluation Framework
DECAT classifies multimodal representations into four diagnostic scenarios using null-referenced metrics and a rule-based procedure to detect shared biology versus confounders without knowing the confounder identity.
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PC-MIL: Decoupling Feature Resolution from Supervision Scale in Whole-Slide Learning
PC-MIL shows that anchoring supervision at a 2 mm scale and progressively mixing slide- and region-level labels improves cross-context accuracy in WSI cancer detection without reducing global performance.
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Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction
A masked-diffusion pretrained convolutional model outperforms ViT pathology foundation models on cell-level dense prediction tasks in histology.