6 citations · 9 across the 9 of their papers we have counts for
11 papers
Towards a Multimodal Large Language Model with Pixel-Level Insight for Biomedicine
Xiaoshuang Huang, Lingdong Shen, Jia Liu +4
In recent years, Multimodal Large Language Models (MLLM) have achieved notable advancements, demonstrating the feasibility of developing an intelligent biomedical assistant. Howeve…
A Refer-and-Ground Multimodal Large Language Model for Biomedicine
Xiaoshuang Huang, Haifeng Huang, Lingdong Shen +4
With the rapid development of multimodal large language models (MLLMs), especially their capabilities in visual chat through refer and ground functionalities, their significance is…
SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging
Lingdong Shen, Fangxin Shang, Xiaoshuang Huang +3
In the field of medical image segmentation, tackling Out-of-Distribution (OOD) segmentation tasks in a cost-effective manner remains a significant challenge. Universal segmentation…
SynFundus-1M: A High-quality Million-scale Synthetic fundus images Dataset with Fifteen Types of Annotation
Fangxin Shang, Jie Fu, Yehui Yang +3
Large-scale public datasets with high-quality annotations are rarely available for intelligent medical imaging research, due to data privacy concerns and the cost of annotations. I…
SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer
Junde Wu, Huihui Fang, Fangxin Shang +5
Clinically, the accurate annotation of lesions/tissues can significantly facilitate the disease diagnosis. For example, the segmentation of optic disc/cup (OD/OC) on fundus image w…
Learning self-calibrated optic disc and cup segmentation from multi-rater annotations
Junde Wu, Huihui Fang, Fangxin Shang +5
The segmentation of optic disc(OD) and optic cup(OC) from fundus images is an important fundamental task for glaucoma diagnosis. In the clinical practice, it is often necessary to…