208 citations · 424 across the 16 of their papers we have counts for
4 papers · 1 filter
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…
MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification
Dequan Wang, Xiaosong Wang, Lilong Wang +12
Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications. Recent advances further enabl…
MedGPTEval: A Dataset and Benchmark to Evaluate Responses of Large Language Models in Medicine
Jie Xu, Lu Lu, Sen Yang +10
METHODS: First, a set of evaluation criteria is designed based on a comprehensive literature review. Second, existing candidate criteria are optimized for using a Delphi method by…
SAM on Medical Images: A Comprehensive Study on Three Prompt Modes
Dongjie Cheng, Ziyuan Qin, Zekun Jiang +3
The Segment Anything Model (SAM) made an eye-catching debut recently and inspired many researchers to explore its potential and limitation in terms of zero-shot generalization capa…