2 citations · 2 across the 5 of their papers we have counts for
7 papers
CORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning
Yuxin Xie, Yuming Chen, Yishan Yang +5
Medical image segmentation is undergoing a paradigm shift from conventional visual pattern matching to cognitive reasoning analysis. Although Multimodal Large Language Models (MLLM…
Uncertainty-aware Cross-training for Semi-supervised Medical Image Segmentation
Kaiwen Huang, Tao Zhou, Huazhu Fu +3
Semi-supervised learning has gained considerable popularity in medical image segmentation tasks due to its capability to reduce reliance on expert-examined annotations. Several mea…
Text-driven Multiplanar Visual Interaction for Semi-supervised Medical Image Segmentation
Kaiwen Huang, Yi Zhou, Huazhu Fu +3
Semi-supervised medical image segmentation is a crucial technique for alleviating the high cost of data annotation. When labeled data is limited, textual information can provide ad…
MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretraining
Ruiqi Wu, Na Su, Chenran Zhang +10
Vision-language pretraining (VLP) has been investigated to generalize across diverse downstream tasks for fundus image analysis. Although recent methods showcase promising achievem…
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation
Kaiwen Huang, Tao Zhou, Huazhu Fu +4
The limited availability of labeled data has driven advancements in semi-supervised learning for medical image segmentation. Modern large-scale models tailored for general segmenta…
DB-SAM: Delving into High Quality Universal Medical Image Segmentation
Chao Qin, Jiale Cao, Huazhu Fu +2
Recently, the Segment Anything Model (SAM) has demonstrated promising segmentation capabilities in a variety of downstream segmentation tasks. However in the context of universal m…