ORACLE-CT improves CT classification performance by using anatomy-specific support pooling based on multi-organ segmentation, showing gains in AUROC on internal and external datasets.
arXiv preprint arXiv:2501.14548 (2025)
5 Pith papers cite this work. Polarity classification is still indexing.
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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.
DCP-PD improves macro F1 scores on CT report generation benchmarks and introduces a hierarchical location-aware evaluation protocol that reveals ongoing challenges in pathology spatial grounding.
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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ORACLE-CT: Anatomy-Aware Support Pooling for CT Classification
ORACLE-CT improves CT classification performance by using anatomy-specific support pooling based on multi-organ segmentation, showing gains in AUROC on internal and external datasets.
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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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Enhancing Fine-Grained Spatial Grounding in 3D CT Report Generation via Discriminative Guidance
DCP-PD improves macro F1 scores on CT report generation benchmarks and introduces a hierarchical location-aware evaluation protocol that reveals ongoing challenges in pathology spatial grounding.
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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.