ArticleScientific reports2025
A vision attention driven Language framework for medical report generation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- An uncertainty-aware vision transformer-BiLSTM Bayesian framework for reliable clinical decision support using chest X-rays.Scientific reports · 2026Article
- MedFusionT5: Cross-Modal Attention Boosts Semantic Quality and Reduces Hallucinations in Dental AI.International dental journal · 2026Article
- Semi-supervised medical image captioning via anatomical collaborative evidence network.Frontiers in medicine · 2026Article
- Multimodal medical diagnosis: a mini review of LLM-vision fusion models in low-resource healthcare settings.Frontiers in digital health · 2026Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study introduces the Medical Vision Attention Generation (MedVAG) model, a novel framework designed to facilitate the automated generation of medical reports. MedVAG integrates Vision Transformer (ViT)-based visual feature extraction and GPT-2 language modeling, enhanced by graph-based feature fusion and multiple attention mechanisms (co-attention, cross-attention, memory-guided attention), to ensure semantic coherence and diagnostic accuracy. Evaluated on IU X-Ray and COV-CTR datasets, the model achieved state-of-the-art performance across natural language generation metrics (BLEU, METEOR, ROUGE, CIDEr) and clinical effectiveness measures. Ablation studies highlighted the critical role of attention mechanisms and feature fusion in aligning visual and textual features. MedVAG demonstrates strong potential as an assistive technology, aiming to support radiologists by reducing workload and enhancing diagnostic accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.