1 code implementation • 11 Jan 2024 • Kunpeng Qiu, Zhiying Zhou, Yongxin Guo
Accurate lesion classification in Wireless Capsule Endoscopy (WCE) images is vital for early diagnosis and treatment of gastrointestinal (GI) cancers.
no code implementations • 9 Oct 2023 • Yongxin Guo, Xiaoying Tang, Tao Lin
To this end, this paper presents a comprehensive investigation into current clustered FL methods and proposes a four-tier framework, namely HCFL, to encompass and extend existing approaches.
no code implementations • 29 Jan 2023 • Yongxin Guo, Xiaoying Tang, Tao Lin
In this paper, we identify the learning challenges posed by the simultaneous occurrence of diverse distribution shifts and propose a clustering principle to overcome these challenges.
1 code implementation • 26 May 2022 • Yongxin Guo, Xiaoying Tang, Tao Lin
As a remedy, we propose FedBR, a novel unified algorithm that reduces the local learning bias on features and classifiers to tackle these challenges.
no code implementations • 5 May 2022 • Weichen Fan, Yuanbo Yang, Kunpeng Qiu, Shuo Wang, Yongxin Guo
Therefore, to address the generalization problem in GI(Gastrointestinal) endoscopy, we propose a multi-domain GI dataset and a light, plug-in block called InvNorm(Invertible Normalization), which could achieve a better generalization performance in any structure.
no code implementations • 25 Dec 2021 • Yongxin Guo, Tao Lin, Xiaoying Tang
Federated Learning (FL) is a learning paradigm that protects privacy by keeping client data on edge devices.