6 papers
Intracranial Aneurysm Classification and Segmentation via Tri-Axial ROI and Multi-Task Learning
Pengcheng Shi, Kaiyuan Yang, Houjing Huang +6
Intracranial aneurysms are often asymptomatic until rupture, which carries high mortality. Rupture risk assessment and treatment planning depend on both aneurysm morphology and ana…
BCL: Bayesian In-Context Learning Framework for Information Extraction
Haoliang Liu, Chengkun Cai, Xu Zhao +7
Existing information extraction (IE) tasks increasingly adopt in-context learning (ICL) with large language models. However, current approaches either show inconsistent performance…
Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
Zhaoyuan Cai, Xinglin Zhang
Federated Unlearning (FU) is an emerging paradigm in Federated Learning (FL) that enables participating clients to fully remove their contributions from a trained global model, dri…
U-VLM: Hierarchical Vision Language Modeling for Report Generation
Pengcheng Shi, Minghui Zhang, Kehan Song +3
Automated radiology report generation is key for reducing radiologist workload and improving diagnostic consistency, yet generating accurate reports for 3D medical imaging remains…
Medal S: Spatio-Textual Prompt Model for Medical Segmentation
Pengcheng Shi, Jiawei Chen, Jiaqi Liu +3
We introduce Medal S, a medical segmentation foundation model that supports native-resolution spatial and textual prompts within an end-to-end trainable framework. Unlike text-only…
Addressing Domain Shift via Imbalance-Aware Domain Adaptation in Embryo Development Assessment
Lei Li, Xinglin Zhang, Jun Liang +1
Deep learning models in medical imaging face dual challenges: domain shift, where models perform poorly when deployed in settings different from their training environment, and cla…