A novel Boltzmann-driven Dynamic Annealing Network for Knowledge-guided Radiology Report Generation that employs Boltzmann distribution with an annealing mechanism to process global visual features, while integrating medical knowledge with dynamic sparse attention for precise lesion identification is proposed.
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
In order to overcome the challenges of incorrect medical terminology application and inaccurate descriptions in the generated reports due to the static character of medical knowledge and rough features combination, this paper will introduce a novel method in the form of the KDMG model of chest X-ray report generation t...
Jie Xiong, Chang-Fa Wei, Hui-Na Liu· Journal of King Saud Univers...· 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
ClinAlign—a memory-based retrieval framework aligned with clinical workflow, drawing inspiration from clinical diagnostic workflows is proposed, which constructs a disease-aware visual memory bank and introduces Classification-Guided Prompt Augmentation (CGPA), where disease state predictions are converted into structu...
Lihong Qiao, Shi-Yi Gao, Yu-Cheng Shu et al.· Proceedings of the Thirty-Fi...· 0 citations
This paper finetune two report generation models with an attention-alignment loss that regularizes the model attention over image patches to match the distribution of pathologist attention, providing stronger visual support for the generated reports by highlighting the regions that most influence the output.
Ruo-Yu Xue, S. Singh, Souradeep Chakraborty et al.· arXiv.org· 0 citations
A clinically curated Pan-Asia WSI--report dataset is introduced and the REG 2025 benchmark is established as a benchmark for evaluating WSI-based structured report generation and vision-language understanding in computational pathology, providing insights for the design of clinically grounded multimodal pathology model...
Yu-Mi Lee, Harim Oh, Hyo-yun Kim et al.· 0 citations
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