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Found 6 papers, 2 papers with code
Learn From Zoom: Decoupled Supervised Contrastive Learning For WCE Image Classification
Accurate lesion classification in Wireless Capsule Endoscopy (WCE) images is vital for early diagnosis and treatment of gastrointestinal (GI) cancers.
Find Your Optimal Assignments On-the-fly: A Holistic Framework for Clustered Federated Learning
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.
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
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.
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction
As a remedy, we propose FedBR, a novel unified algorithm that reduces the local learning bias on features and classifiers to tackle these challenges.
InvNorm: Domain Generalization for Object Detection in Gastrointestinal Endoscopy
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.
Towards Federated Learning on Time-Evolving Heterogeneous Data
Federated Learning (FL) is a learning paradigm that protects privacy by keeping client data on edge devices.
