52 citations · 132 across the 15 of their papers we have counts for
5 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…
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…
FCN+: Global Receptive Convolution Makes FCN Great Again
Xiaoyu Ren, Zhongying Deng, Jin Ye +2
Fully convolutional network (FCN) is a seminal work for semantic segmentation. However, due to its limited receptive field, FCN cannot effectively capture global context informatio…