Visual-linguistic Diagnostic Semantic Enhancement for medical report generation

Generative methods are currently popular for medical report generation, as they automatically generate professional reports from input images, assisting physicians in making faster and more accurate decisions. However, current methods face significant challenges: 1) Lesion areas in medical images are often difficult for models to capture accurately, and 2) even when captured, these areas are frequently not described using precise clinical diagnostic terms. To address these problems, we propose a Visual-Linguistic Diagnostic Semantic Enhancement model (VLDSE) to generate high-quality reports. Our approach employs supervised contrastive learning in the Image and Report Semantic Consistency (IRSC) module to bridge the semantic gap between visual and linguistic features. Additionally, we design the Visual Semantic Qualification and Quantification (VSQQ) module and the Post-hoc Semantic Correction (PSC) module to enhance visual semantics and inter-word relationships, respectively. Experiments demonstrate that our model achieves promising performance on the publicly available IU X-RAY and MIMIC-MV datasets. Specifically, on the IU X-RAY dataset, our model achieves a BLEU-4 score of 18.6%, improving the baseline by 12.7%. On the MIMIC-MV dataset, our model improves the BLEU-1 score by 10.7% over the baseline. These results demonstrate the ability of our model to generate accurate and fluent descriptions of lesion areas.