This study proposes MG-Hybrid to harmonize computational efficiency with anatomical fidelity while bridging the semantic gap between visual features and clinical text, a Mask-Guided Visual Disentanglement strategy that extracts structured organ-specific prototypes to preserve the spatial integrity of regions such as the heart and lungs.
SMG-Mamba provides a highly efficient and accurate solution for text-guided medical image segmentation by mapping specific modules to computational efficiency, boundary preservation, and deep semantic alignment, and successfully balances computational overhead with precise, fine-grained cross-modal feature fusion.
Rong-Tao Li, De-Dong Yang, Zi-Yang Li et al.· Multimedia Systems· 0 citations
Generating clinically accurate radiology reports from chest X-rays demands both precise pathology recognition and coherent medical language. However, fine-tuning large vision-language models can be computationally challenging in deployment settings. We present a lightweight framework that improves report generation fro...
Taishi Nishizawa, Ayesha Issah, Ana Fuertes-Brito et al.· IEEE/ACM International Confe...· 0 citations
A unified framework for automatic report generation from SPECT bone scintigrams that integrates domain-adaptive representation learning, fine-grained image–text alignment, and anatomy-guided supervision is proposed, offering a valuable pathway to achieving trustworthy and intelligent diagnostic support within nuclear m...
Tao Song, Qiang Lin, Tong-Tong Li et al.· Applied intelligence (Boston...· 0 citations
Experiments on BUS-CoT and IU X-ray datasets demonstrate consistent improvements in diagnostic accuracy, concept consistency, and report quality over strong general-purpose and medical MLLMs, indicating that concept-grounded reasoning better aligns generation with clinical decision processes.
Xin-Yue Xu, Hong-Bin Lin, Juan-Gui Xu et al.· 0 citations
A Concept Clause Decomposition method is designed to extract semantically complete descriptions of pathological findings or radiology manifestations from medical reports as medical concept clauses, which are then utilized within a multi-granularity cross-modal alignment framework to enhance medical concept perception a...
Xiang-Min Kong, Xi-Bin Jia, Da-Wei Yang et al.· IEEE journal of biomedical a...· 0 citations
The proposed AG-VLM framework provides a scalable foundation for computer-assisted radiology reporting while retaining the need for radiologist verification before clinical use and indicates that explicit attention-guided visual reasoning combined with cross-modal semantic alignment can generate more accurate, clinical...
P. Dayaker, M. Vignesh, I. Z. et al.· International journal of com...· 0 citations
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