A sequential diffusion framework generates controllable abdominal anatomies with a Volume Control Scalar that decouples organ size from body habitus, achieving Dice scores around 0.83 and reducing distributional mismatch by 73.6% in a hepatomegaly example.
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JANUS conditions Vision Transformer embeddings on macro-radiomic priors via anatomically guided gating, reaching macro-AUROC 0.88 on an internal test set of 5082 cases and 0.87 on an external set of 2000 cases while improving calibration and reducing high-confidence false positives under domainshift
CT-SpatialVQA benchmark reveals that eight 3D medical VLMs achieve only 34% average accuracy on semantic-spatial reasoning tasks from CT data, frequently below random performance.
MedConcept extracts reusable medical concepts from VLMs via sparse neuron activations, translates them to pseudo-reports, and scores them for semantic alignment using an independent medical LLM.
CA-GCL combines global contrastive learning with permutation-invariant text augmentation to deliver zero-shot 3D medical abnormality detection that is more robust to prompt changes than prior FVLP methods.
citing papers explorer
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AbdomenGen: Sequential Volume-Conditioned Diffusion Framework for Abdominal Anatomy Generation
A sequential diffusion framework generates controllable abdominal anatomies with a Volume Control Scalar that decouples organ size from body habitus, achieving Dice scores around 0.83 and reducing distributional mismatch by 73.6% in a hepatomegaly example.
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JANUS: Anatomy-Conditioned Gating for Robust CT Triage Under Distribution Shift
JANUS conditions Vision Transformer embeddings on macro-radiomic priors via anatomically guided gating, reaching macro-AUROC 0.88 on an internal test set of 5082 cases and 0.87 on an external set of 2000 cases while improving calibration and reducing high-confidence false positives under domainshift
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Lost in Volume: The CT-SpatialVQA Benchmark for Evaluating Semantic-Spatial Understanding of 3D Medical Vision-Language Models
CT-SpatialVQA benchmark reveals that eight 3D medical VLMs achieve only 34% average accuracy on semantic-spatial reasoning tasks from CT data, frequently below random performance.
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MedConcept: Unsupervised Concept Discovery for Interpretability in Medical VLMs
MedConcept extracts reusable medical concepts from VLMs via sparse neuron activations, translates them to pseudo-reports, and scores them for semantic alignment using an independent medical LLM.
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CA-GCL: Cross-Anatomy Global-Local Contrastive Learning for Robust 3D Medical Image Understanding
CA-GCL combines global contrastive learning with permutation-invariant text augmentation to deliver zero-shot 3D medical abnormality detection that is more robust to prompt changes than prior FVLP methods.