52 citations · 150 across the 20 of their papers we have counts for
9 papers · 2 filters
SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images
Haoyu Wang, Sizheng Guo, Jin Ye +11
Existing volumetric medical image segmentation models are typically task-specific, excelling at specific target but struggling to generalize across anatomical structures or modalit…
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…
Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation
Yuncheng Yang, Meng Wei, Junjun He +3
Transfer learning is a critical technique in training deep neural networks for the challenging medical image segmentation task that requires enormous resources. With the abundance…
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…
STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training
Ziyan Huang, Haoyu Wang, Zhongying Deng +8
Large-scale models pre-trained on large-scale datasets have profoundly advanced the development of deep learning. However, the state-of-the-art models for medical image segmentatio…
Token Sparsification for Faster Medical Image Segmentation
Lei Zhou, Huidong Liu, Joseph Bae +3
Can we use sparse tokens for dense prediction, e.g., segmentation? Although token sparsification has been applied to Vision Transformers (ViT) to accelerate classification, it is s…