726 citations · 1.9k across the 53 of their papers we have counts for
53 papers
SAM-Med2D
Junlong Cheng, Jin Ye, Zhongying Deng +12
The Segment Anything Model (SAM) represents a state-of-the-art research advancement in natural image segmentation, achieving impressive results with input prompts such as points an…
LLM-Mini-CEX: Automatic Evaluation of Large Language Model for Diagnostic Conversation
Xiaoming Shi, Jie Xu, Jinru Ding +9
There is an increasing interest in developing LLMs for medical diagnosis to improve diagnosis efficiency. Despite their alluring technological potential, there is no unified and co…
Text-guided Foundation Model Adaptation for Pathological Image Classification
Yunkun Zhang, Jin Gao, Mu Zhou +4
The recent surge of foundation models in computer vision and natural language processing opens up perspectives in utilizing multi-modal clinical data to train large models with str…
MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset
Guotai Wang, Jianghao Wu, Xiangde Luo +3
Pretraining with large-scale 3D volumes has a potential for improving the segmentation performance on a target medical image dataset where the training images and annotations are l…
Exploring Data Redundancy in Real-world Image Classification through Data Selection
Zhenyu Tang, Shaoting Zhang, Xiaosong Wang
Deep learning models often require large amounts of data for training, leading to increased costs. It is particularly challenging in medical imaging, i.e., gathering distributed da…
UM-CAM: Uncertainty-weighted Multi-resolution Class Activation Maps for Weakly-supervised Fetal Brain Segmentation
Jia Fu, Tao Lu, Shaoting Zhang +1
Accurate segmentation of the fetal brain from Magnetic Resonance Image (MRI) is important for prenatal assessment of fetal development. Although deep learning has shown the potenti…