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.
Artificial intelligence for diagnosis and gleason grading of prostate cancer: the panda challenge.Nature medicine, 28(1):154–163
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SlideMix improves whole slide image classification by using a vision-language model to select diagnostically relevant regions and then shuffling tile features between slides with adaptive curriculum learning.
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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SlideMix: Enhancing Whole Slide Image Analysis via Multimodal Shuffling
SlideMix improves whole slide image classification by using a vision-language model to select diagnostically relevant regions and then shuffling tile features between slides with adaptive curriculum learning.
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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.