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 medicine.
Radiology reports are vital for accurate diagnosis and treatment planning, yet their manual generation is time-consuming and dependent on radiologist expertise, leading to delays and inconsistent clinical decisions. Medical image–text retrieval offers a scalable solution by enabling the retrieval of relevant prior case...
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
This work proposes MedRecord-CLIP, a knowledge-enhanced foundation model featuring a diagnosis-guided cross-attention mechanism to adaptively extract and fuse salient patient history with diagnostic representations that highlights the critical value of integrating personalized clinical context to enhance the generaliza...
Lei Shi, Wenbin Zhai, Lei Yu et al.· Health Information Science a...· 0 citations
SeVeR is proposed, a selective visual exposure framework that compresses dense volumes into modality-wise prototypes and retrieves complementary multi-level evidence with change-aware gated attention during decoding, trained with a marginal-utility self-consistency objective that suppresses unhelpful retrieval.
Yao-Jun Hu, Danyang Tu, Yang Liu et al.· 0 citations
Medical image segmentation is still largely treated as a vision-only problem, although clinical interpretation often relies on textual knowledge of anatomy, location, appearance, and surrounding context. Existing text-guided segmentation methods within the Vision-Language Model (VLM) paradigm often use language only as...
Rafi Ibn Sultan, Hui Zhu, Chengyin Li et al.· 0 citations
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