SAM-family models split into occluder-aware types that avoid predicting into occluded regions and occluder-agnostic types that confidently segment hidden areas, shown via a new benchmark on polyp datasets.
Medsam: Segment anything in medical images
4 Pith papers cite this work. Polarity classification is still indexing.
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CardioSAM introduces a topology-aware decoder and boundary refinement module to elevate SAM's performance on cardiac MRI, achieving a 93.39% Dice score.
Fine-tuned foundation models produce reliable MSK MRI biomarkers that support workload-reducing triage and calibrated 48-month prediction of knee replacement and incident OA.
The paper surveys data-centric strategies for foundation models in computational healthcare and supplies a curated list of related models and datasets.
citing papers explorer
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Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models
SAM-family models split into occluder-aware types that avoid predicting into occluded regions and occluder-agnostic types that confidently segment hidden areas, shown via a new benchmark on polyp datasets.
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CardioSAM: Topology-Aware Decoder Design for High-Precision Cardiac MRI Segmentation
CardioSAM introduces a topology-aware decoder and boundary refinement module to elevate SAM's performance on cardiac MRI, achieving a 93.39% Dice score.
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Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes
Fine-tuned foundation models produce reliable MSK MRI biomarkers that support workload-reducing triage and calibrated 48-month prediction of knee replacement and incident OA.
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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.