CA-GCL adds global contrastive separation and clinical text augmentation to fine-grained vision-language pretraining, reducing textual embedding collapse and prompt variance in 3D medical image tasks.
arXiv preprint arXiv:2404.15272 (2024) 3
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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.
citing papers explorer
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CA-GCL: Cross-Anatomy Global-Local Contrastive Learning for Robust 3D Medical Image Understanding
CA-GCL adds global contrastive separation and clinical text augmentation to fine-grained vision-language pretraining, reducing textual embedding collapse and prompt variance in 3D medical image 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.