activity
20192026
most citedFocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images

96 citations · 464 across the 30 of their papers we have counts for

collaborators
Showing 2025Show all

6 papers · 1 filter

cs.CV2025

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation

Ziyu Zhang, Yi Yu, Simeng Zhu +4

Accurate segmentation of anatomical structures in medical images is essential for diagnosis and treatment planning. While recent interactive segmentation foundation models enhance…

cs.CV2025

Anatomy-VLM: A Fine-grained Vision-Language Model for Medical Interpretation

Difei Gu, Yunhe Gao, Mu Zhou +1

Accurate disease interpretation from radiology remains challenging due to imaging heterogeneity. Achieving expert-level diagnostic decisions requires integration of subtle image fe…

cs.CV2025

K-Prism: A Knowledge-Guided and Prompt Integrated Universal Medical Image Segmentation Model

Bangwei Guo, Yunhe Gao, Meng Ye +4

Medical image segmentation is fundamental to clinical decision-making, yet existing models remain fragmented. They are usually trained on single knowledge sources and specific to i…

cs.CV2025

Show and Segment: Universal Medical Image Segmentation via In-Context Learning

Yunhe Gao, Di Liu, Zhuowei Li +4

Medical image segmentation remains challenging due to the vast diversity of anatomical structures, imaging modalities, and segmentation tasks. While deep learning has made signific…

cs.CV2025

The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models via Visual Information Steering

Zhuowei Li, Haizhou Shi, Yunhe Gao +7

Large Vision-Language Models (LVLMs) can reason effectively over both textual and visual inputs, but they tend to hallucinate syntactically coherent yet visually ungrounded content…

cs.CV2025★ 1 cited

RadAlign: Advancing Radiology Report Generation with Vision-Language Concept Alignment

Difei Gu, Yunhe Gao, Yang Zhou +2

Automated chest radiographs interpretation requires both accurate disease classification and detailed radiology report generation, presenting a significant challenge in the clinica…