40 citations · 87 across the 20 of their papers we have counts for
7 papers · 1 filter
MedUHIP: Towards Human-In-the-Loop Medical Segmentation
Jiayuan Zhu, Junde Wu
Although segmenting natural images has shown impressive performance, these techniques cannot be directly applied to medical image segmentation. Medical image segmentation is partic…
Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruning
Haofeng Liu, Erli Zhang, Junde Wu +2
Surgical video segmentation is a critical task in computer-assisted surgery and is vital for enhancing surgical quality and patient outcomes. Recently, the Segment Anything Model 2…
Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation
Junde Wu, Jiayuan Zhu, Yunli Qi +4
We introduce a novel graph-based Retrieval-Augmented Generation (RAG) framework specifically designed for the medical domain, called \textbf{MedGraphRAG}, aimed at enhancing Large…
Medical SAM 2: Segment medical images as video via Segment Anything Model 2
Jiayuan Zhu, Abdullah Hamdi, Yunli Qi +2
Medical image segmentation plays a pivotal role in clinical diagnostics and treatment planning, yet existing models often face challenges in generalization and in handling both 2D…
MGI: Multimodal Contrastive pre-training of Genomic and Medical Imaging
Jiaying Zhou, Mingzhou Jiang, Junde Wu +3
Medicine is inherently a multimodal discipline. Medical images can reflect the pathological changes of cancer and tumors, while the expression of specific genes can influence their…
Not just Birds and Cars: Generic, Scalable and Explainable Models for Professional Visual Recognition
Junde Wu, Jiayuan Zhu, Min Xu +1
Some visual recognition tasks are more challenging then the general ones as they require professional categories of images. The previous efforts, like fine-grained vision classific…
Towards Collective Intelligence: Uncertainty-aware SAM Adaptation for Ambiguous Medical Image Segmentation
Mingzhou Jiang, Jiaying Zhou, Junde Wu +3
Collective intelligence from multiple medical experts consistently surpasses individual expertise in clinical diagnosis, particularly for ambiguous medical image segmentation tasks…