Towards Clinically Faithful Medical Image Captioning via Enhanced Vision-Language Alignment
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2608.19825 (cs)
[Submitted on 20 Aug 2026]
Title:Towards Clinically Faithful Medical Image Captioning via Enhanced Vision-Language Alignment
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Abstract:Medical image captioning is a technique that accelerates early-stage diagnostic workflows and enhances the interpretability of medical diagnostic AI systems. However, unlike general image captioning, clinically reliable captioning remains challenging due to grayscale-based modalities, subtle anatomical cues, specialized medical phrasing, and variations in data quality. Despite recent advances in large vision-language models, fluent outputs do not necessarily guarantee sufficient alignment with clinical concept spaces or evaluation criteria. To address this issue, we propose a framework that strengthens clinical alignment by separating and enhancing training-time alignment and inference-time alignment. We build a medical image captioning pipeline that integrates single/dual vision encoders based on BioMedCLIP and SigLIP2, a Q-Former, and a LLaMA-based decoder, and examine the contribution of auxiliary learning for UMLS concept/type prediction. At inference, we apply single-embedding-based reranking to select the best caption among candidates, while at training we introduce MedPAIR-SCST, which combines clinically relevant rewards to shift the generative distribution toward improved clinical alignment. Our experiments show that complementary visual representations with a multi-encoder design and concept-level auxiliary learning help preserve clinically meaningful information. Furthermore, inference-time reranking provides a practical way to improve semantic and clinical alignment without additional training, whereas MedPAIR-SCST goes beyond selection by directly improving the model's distribution to generate more consistent and clinically grounded captions. These findings suggest that jointly leveraging selection-based alignment and reinforcement-learning-based alignment can promote more trustworthy medical image captioning even in data-constrained settings.
| Comments: | 10 pages, 2 figures, 7 tables. Preprint submitted to IEEE for possible publication |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.19825 [cs.CV] |
| (or arXiv:2608.19825v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2608.19825
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Towards Clinically Faithful Medical Image Captioning via Enhanced Vision-Language Alignment, by Yunseo Lee and 3 other authors
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