1 citations · 1 across the 6 of their papers we have counts for
8 papers
Separating Capability from Confidence: Grounded Dual-State Calibration for GRPO-Trained Medical Vision-Language Models
Yangyang Xie, Ke Hao, Jiaqi Liu +2
Medical vision-language models (VLMs) require confidence that reflects both answer correctness and patient-specific visual evidence. Recent GRPO-based methods optimize verbalized c…
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…
Intracranial Aneurysm Classification and Segmentation via Tri-Axial ROI and Multi-Task Learning
Pengcheng Shi, Kaiyuan Yang, Houjing Huang +7
Intracranial aneurysms are often asymptomatic until rupture, which carries high mortality. Rupture risk assessment and treatment planning depend on both aneurysm morphology and ana…
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…
MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training with Masked Autoencoder
Lei Li, Tianfang Zhang, Xinglin Zhang +4
Within the domain of medical analysis, extensive research has explored the potential of mutual learning between Masked Autoencoders(MAEs) and multimodal data. However, the impact o…