BatMIL uses hybrid hyperbolic-Euclidean geometry, an S4 state-space backbone, and chunk-level mixture-of-experts to outperform prior multiple-instance learning methods on seven whole-slide image datasets across six cancers.
Towards a general-purpose foundation model for computational pathology.Nature Medicine
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
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.
Post-hoc isotonic regression calibration for deep Cox survival models that improves calibration with theoretical guarantees including double-robustness and asymptotic calibration.
SSMamba uses a two-stage self-supervised pretraining and fine-tuning pipeline with Mamba-based components to outperform prior pathological foundation models on ROI and WSI classification tasks.
citing papers explorer
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Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation
BatMIL uses hybrid hyperbolic-Euclidean geometry, an S4 state-space backbone, and chunk-level mixture-of-experts to outperform prior multiple-instance learning methods on seven whole-slide image datasets across six cancers.
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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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Isotonic Survival Regression: Calibrated Survival Distributions from Deep Cox Models
Post-hoc isotonic regression calibration for deep Cox survival models that improves calibration with theoretical guarantees including double-robustness and asymptotic calibration.
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SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification
SSMamba uses a two-stage self-supervised pretraining and fine-tuning pipeline with Mamba-based components to outperform prior pathological foundation models on ROI and WSI classification tasks.