SemEnrich: Self-Supervised Semantic Enrichment of Radiology Reports for Vision-Language Learning

ArXi:2604.09887v1 Announce Type: new Medical vision-language datasets are often limited in size and biased toward negative findings, as clinicians report abnormalities mostly but might omit some positive/neutral findings because they might be considered as irrelevant to the patient's condition. We propose a self-supervised data enrichment method that leverages semantic clustering of report sentences. Then we enrich the findings in the medical reports in the