ConQuer augments global CLIP alignment with independent per-concept contrastive losses on anatomical regions extracted from reports, producing Jolia which outperforms CLIP baselines on classification, report generation, and transfer.
arXiv preprint arXiv:2404.15272 (2024)
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
ASAP introduces an anatomy-aware semantically-adaptive pre-training method for medical volumetric vision-language models and reports state-of-the-art results on a new benchmark spanning 15 datasets and 22 tasks.
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.
Adapts Grounding DINO to 3D CT for localizing liver, spleen, kidneys and bowel using pseudo-text conditioning, achieving 0.583 mAP on 193 volumes.
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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Jolia: Concept-Level Vision-Language Alignment for 3D CT Contrastive Learning
ConQuer augments global CLIP alignment with independent per-concept contrastive losses on anatomical regions extracted from reports, producing Jolia which outperforms CLIP baselines on classification, report generation, and transfer.
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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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ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training
ASAP introduces an anatomy-aware semantically-adaptive pre-training method for medical volumetric vision-language models and reports state-of-the-art results on a new benchmark spanning 15 datasets and 22 tasks.
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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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Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT
Adapts Grounding DINO to 3D CT for localizing liver, spleen, kidneys and bowel using pseudo-text conditioning, achieving 0.583 mAP on 193 volumes.
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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.