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arXiv Machine Learning By Halil Ibrahim Gulluk, Olivier Gevaert

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

Read the original on arXiv Machine Learning →

SemEnrich introduces a self‑supervised method to enrich radiology reports by clustering sentences semantically and adding positive or neutral observations from different clusters. The enriched data consistently improves supervised fine‑tuning across multiple vision‑language metrics, with gains ranging from 3% to 7.5% on COMET, Bert, Sentence Bleu, CheXbert‑F1, and RadGraph‑F1. The authors also demonstrate that incorporating cluster information into the reward design for GRPO training yields additional performance boosts.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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