Federated and Transfer Learning for Cancer Detection Based on Image Analysis

TL;DR

This review explores how federated and transfer learning enhance cancer detection through image analysis, emphasizing their roles, benefits, limitations, and future research directions in improving diagnostic accuracy.

Contribution

It provides a comprehensive assessment of federated and transfer learning methods in image-based cancer detection, highlighting their applications, strengths, and weaknesses.

Findings

Federated learning enables privacy-preserving model training across multiple sites.

Transfer learning improves diagnostic accuracy with limited data.

The paper discusses future research directions in this field.

Abstract

This review article discusses the roles of federated learning (FL) and transfer learning (TL) in cancer detection based on image analysis. These two strategies powered by machine learning have drawn a lot of attention due to their potential to increase the precision and effectiveness of cancer diagnosis in light of the growing importance of machine learning techniques in cancer detection. FL enables the training of machine learning models on data distributed across multiple sites without the need for centralized data sharing, while TL allows for the transfer of knowledge from one task to another. A comprehensive assessment of the two methods, including their strengths, and weaknesses is presented. Moving on, their applications in cancer detection are discussed, including potential directions for the future. Finally, this article offers a thorough description of the functions of TL and FL in image-based cancer detection. The authors also make insightful suggestions for additional study in this rapidly developing area.