10 citations · 11 across the 7 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…
Spectral Machine Learning for Pancreatic Mass Imaging Classification
Yiming Liu, Ying Chen, Guangming Pan +5
We present a novel spectral machine learning (SML) method in screening for pancreatic mass using CT imaging. Our algorithm is trained with approximately 30,000 images from 250 pati…
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 Consistency Theorem for Randomized Singular Value Decomposition
Ting-Li Chen, Su-Yun Huang, Weichung Wang
The singular value decomposition (SVD) and the principal component analysis are fundamental tools and probably the most popular methods for data dimension reduction. The rapid grow…
Integrating multiple random sketches for singular value decomposition
Ting-Li Chen, Dawei D. Chang, Su-Yun Huang +3
The singular value decomposition (SVD) of large-scale matrices is a key tool in data analytics and scientific computing. The rapid growth in the size of matrices further increases…