31 citations · 39 across the 6 of their papers we have counts for
7 papers
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach
Pochuan Wang, Chen Shen, Masahiro Oda +4
In medical imaging, developing generalized segmentation models that can handle multiple organs and lesions is crucial. However, the scarcity of fully annotated datasets and strict…
ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data
Pochuan Wang, Chen Shen, Weichung Wang +4
Developing a generalized segmentation model capable of simultaneously delineating multiple organs and diseases is highly desirable. Federated learning (FL) is a key technology enab…
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Chen Shen, Pochuan Wang, Holger R. Roth +9
Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their…
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
Pochuan Wang, Chen Shen, Holger R. Roth +9
The performance of deep learning-based methods strongly relies on the number of datasets used for training. Many efforts have been made to increase the data in the medical image an…
A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation
Holger R. Roth, Chen Shen, Hirohisa Oda +5
Recent advances in deep learning, like 3D fully convolutional networks (FCNs), have improved the state-of-the-art in dense semantic segmentation of medical images. However, most ne…
Deep learning and its application to medical image segmentation
Holger R. Roth, Chen Shen, Hirohisa Oda +4
One of the most common tasks in medical imaging is semantic segmentation. Achieving this segmentation automatically has been an active area of research, but the task has been prove…