Anatomical Attention Alignment representation for Radiology Report Generation
This work addresses the problem of generating accurate and clinically relevant radiology reports to reduce radiologists' workload and improve diagnostic services, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled the problem of automated radiology report generation by addressing limitations in understanding spatial structures and semantic relationships in existing models, resulting in A3Net, which improved visual perception and text generation quality on IU X-Ray and MIMIC-CXR datasets.
Automated Radiology report generation (RRG) aims at producing detailed descriptions of medical images, reducing radiologists' workload and improving access to high-quality diagnostic services. Existing encoder-decoder models only rely on visual features extracted from raw input images, which can limit the understanding of spatial structures and semantic relationships, often resulting in suboptimal text generation. To address this, we propose Anatomical Attention Alignment Network (A3Net), a framework that enhance visual-textual understanding by constructing hyper-visual representations. Our approach integrates a knowledge dictionary of anatomical structures with patch-level visual features, enabling the model to effectively associate image regions with their corresponding anatomical entities. This structured representation improves semantic reasoning, interpretability, and cross-modal alignment, ultimately enhancing the accuracy and clinical relevance of generated reports. Experimental results on IU X-Ray and MIMIC-CXR datasets demonstrate that A3Net significantly improves both visual perception and text generation quality. Our code is available at \href{https://github.com/Vinh-AI/A3Net}{GitHub}.