3 papers
eess.IV2023
Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation
Haoran Shen, Yifu Zhang, Wenxuan Wang +4
Recent works have shown that the computational efficiency of 3D medical image (e.g. CT and MRI) segmentation can be impressively improved by dynamic inference based on slice-wise c…
cs.CV2023
Multi-view Self-supervised Disentanglement for General Image Denoising
Hao Chen, Chenyuan Qu, Yu Zhang +2
With its significant performance improvements, the deep learning paradigm has become a standard tool for modern image denoisers. While promising performance has been shown on seen…
cs.CV2023
Med-Tuning: A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation
Jiachen Shen, Wenxuan Wang, Chen Chen +5
The "pre-training then fine-tuning (FT)" paradigm is widely adopted to boost the model performance of deep learning-based methods for medical volumetric segmentation. However, conv…