GoD uses anatomy graphs and difference alignment to improve medical image re-identification accuracy and auditability, with +7.1 pp Rank-1 gains on fundus and +3.1 pp on CXR.
et al.: Feedback on a publicly distributed image database: the Messidor database.Image Analysis and Stereology33(3), 231–234 (2014)
3 Pith papers cite this work, alongside 1,390 external citations. Polarity classification is still indexing.
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Four per-lesion YOLO specialist detectors, each independently configured and reconciled by size/priority suppression, reach 0.527 mAP50 and 0.529 F1 on IDRiD and beat a shared four-class model on all four lesions.
Retina-RAG combines a retinal classifier, LoRA-tuned Qwen2.5-VL, and RAG to jointly grade DR, detect ME, and generate reports, reaching F1 scores of 0.731 and 0.948 while exceeding baselines on ROUGE-L and SBERT metrics.
citing papers explorer
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Graph-of-Differences: Anatomy-Structured Difference Alignment for Medical Image Re-Identification
GoD uses anatomy graphs and difference alignment to improve medical image re-identification accuracy and auditability, with +7.1 pp Rank-1 gains on fundus and +3.1 pp on CXR.
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PRISM-DR: Per-lesion Retinal Inference with Specialist Models for Diabetic Retinopathy
Four per-lesion YOLO specialist detectors, each independently configured and reconciled by size/priority suppression, reach 0.527 mAP50 and 0.529 F1 on IDRiD and beat a shared four-class model on all four lesions.
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Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation
Retina-RAG combines a retinal classifier, LoRA-tuned Qwen2.5-VL, and RAG to jointly grade DR, detect ME, and generate reports, reaching F1 scores of 0.731 and 0.948 while exceeding baselines on ROUGE-L and SBERT metrics.