Automated medical image captioning translates complex radiological images into diagnostic narratives that can support reporting workflows. We present a Swin-BART encoder-decoder system with a lightweight regional attention module that amplifies diagnostically salient regions before cross-attention. Trained and evaluated on ROCO, our model achieves state-of-the-art semantic fidelity while remaining compact and interpretable. We report results as mean±std over three seeds and include 95% confidence intervals. Compared with baselines, our approach improves ROUGE (proposed 0.603, ResNet-CNN 0.356, BLIP2-OPT 0.255) and BERTScore (proposed 0.807, BLIP2-OPT 0.645, ResNet-CNN 0.623), with competitive BLEU, CIDEr, and METEOR. We further provide ablations (regional attention on/off and token-count sweep), per-modality analysis (CT/MRI/X-ray), paired significance tests, and qualitative heatmaps that visualize the regions driving each description. Decoding uses beam search (beam size =4), length penalty =1.1, no_repeat_ngram_size=3, and max length =128. The proposed design yields accurate, clinically phrased captions and transparent regional attributions, supporting safe research use with a human in the loop.
@article{arxiv.2511.09893,
title = {Regional Attention-Enhanced Swin Transformer for Clinically Relevant Medical Image Captioning},
author = {Zubia Naz and Farhan Asghar and Muhammad Ishfaq Hussain and Yahya Hadadi and Muhammad Aasim Rafique and Wookjin Choi and Moongu Jeon},
journal= {arXiv preprint arXiv:2511.09893},
year = {2025}
}