1 code implementation • 8 Aug 2023 • Pochuan Wang, Chen Shen, Weichung Wang, Masahiro Oda, Chiou-Shann Fuh, Kensaku MORI, Holger R. Roth
Federated learning (FL) is a key technology enabling the collaborative development of a model without exchanging training data.
no code implementations • 19 Aug 2021 • Chen Shen, Pochuan Wang, Holger R. Roth, Dong Yang, Daguang Xu, Masahiro Oda, Weichung Wang, Chiou-Shann Fuh, Po-Ting Chen, Kao-Lang Liu, Wei-Chih Liao, Kensaku MORI
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 data.
no code implementations • 22 Jul 2021 • Xiaofeng Liu, Fangxu Xing, Hanna K. Gaggin, Weichung Wang, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo
Assessment of cardiovascular disease (CVD) with cine magnetic resonance imaging (MRI) has been used to non-invasively evaluate detailed cardiac structure and function.
no code implementations • 3 May 2021 • Yiming Liu, Ying Chen, Guangming Pan, Weichung Wang, Wei-Chih Liao, Yee Liang Thian, Cheng E. Chee, Constantinos P. Anastassiades
Factors that influenced high performance of a well-designed integration of spectral learning and machine learning included: 1) use of eigenvectors corresponding to several of the largest eigenvalues of sample covariance matrix (spike eigenvectors) to choose input attributes in classification training, taking into account only the fundamental information of the raw images with less noise; 2) removal of irrelevant pixels based on mean-level spectral test to lower the challenges of memory capacity and enhance computational efficiency while maintaining superior classification accuracy; 3) adoption of state-of-the-art machine learning classification, gradient boosting and random forest.