FOCI adds a post-hoc readout to frozen WSI-MIL models to find compact output-consistent tile subsets and measures selection headroom with SHI, showing transformer-based models allow smaller rationales than attention-pooling baselines.
Data-efficient and weakly supervised computational pathology on whole-slide images.Nature biomedical engineering, 5(6):555–570
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MIST augments MIL projection layers with cross-modal gene-expression prototypes derived from spatial transcriptomics, yielding consistent gains on survival, subtyping, and biomarker tasks across 23 endpoints and 8 aggregators.
MambaBack is a hybrid Mamba-CNN model with Hilbert sampling and chunked inference that reports better performance than seven prior methods on five whole-slide image datasets.
A masked-diffusion pretrained convolutional model outperforms ViT pathology foundation models on cell-level dense prediction tasks in histology.
One-class methods DSVDD and DROC outperform MIL baselines for instance-level detection of rare malignant cells at witness rates ≤1% on bone marrow and oral cytology datasets.
citing papers explorer
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Are Compact Rationales Free? Measuring Tile Selection Headroom in Frozen WSI-MIL
FOCI adds a post-hoc readout to frozen WSI-MIL models to find compact output-consistent tile subsets and measures selection headroom with SHI, showing transformer-based models allow smaller rationales than attention-pooling baselines.
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Bridging the Modality Bottleneck in Pathology MIL through Virtual Molecular Staining
MIST augments MIL projection layers with cross-modal gene-expression prototypes derived from spatial transcriptomics, yielding consistent gains on survival, subtyping, and biomarker tasks across 23 endpoints and 8 aggregators.
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MambaBack: Bridging Local Features and Global Contexts in Whole Slide Image Analysis
MambaBack is a hybrid Mamba-CNN model with Hilbert sampling and chunked inference that reports better performance than seven prior methods on five whole-slide image datasets.
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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.
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Needle in a Haystack: One-Class Representation Learning for Detecting Rare Malignant Cells in Computational Cytology
One-class methods DSVDD and DROC outperform MIL baselines for instance-level detection of rare malignant cells at witness rates ≤1% on bone marrow and oral cytology datasets.