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Federated Learning and Meta-Learning for Medical Image Classification: Effective Aggregation Techniques and Adaptive Learning Rate Approaches

Date

2024-08-23

Journal Title

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Type

Thesis

Degree Level

Masters

Abstract

Computer-aided diagnosis (CAD) has the potential to revolutionize medical diagnosis through advanced machine learning (ML) algorithms. However, the efficacy of CAD systems relies heavily on extensive, diverse, and annotated datasets for robust training. This dissertation proposes a multifaceted strategy to address data scarcity, privacy concerns, and diverse data distribution across healthcare institutions by harnessing two major types of learning methods: federated learning (FL) and model-agnostic meta-learning (MAML). In the first learning method, FL enables CAD systems to train on diverse data across multiple institutions, overcoming individual dataset limitations. However, data heterogeneity (e.g., due to non-independent and identically distributed data) among clients causes client model divergence, making it challenging to achieve convergence during model aggregation. To address these challenges, we propose different aggregation techniques: randomsampling federated maximum (FedRSMax), random-sampling federated median (FedRSMed), and federated rank-based client selection aggregation (FedRSA). In the second learning method, MAML has the potential to facilitate swift adaptation to varying medical imaging datasets, effectively navigating discrepancies in imaging protocols and disease presentations. However, MAML struggles with fixed learning rates, limiting adaptation to varying task complexities. To address this challenge, we propose a task similarity-driven meta-adaptive learning rate mechanism. Overall, this research pursues a two-fold strategy: (1) to develop an efficient aggregation technique for medical image classification using FL; and (2) to employ MAML within a few-shot learning framework, optimizing adaptation to datasets with limited samples. The proposed methodologies are evaluated using a comprehensive set of performance metrics, including accuracy, convergence rate, and adaptation loss. By integrating these cutting-edge techniques, this dissertation advances the frontier of CAD systems, paving the way for more accurate, adaptable, and scalable solutions in medical image analysis.

Description

Keywords

Medical Imaging, Federated Learning, Meta Learning, Classification

Citation

Degree

Master of Science (M.Sc.)

Department

Biomedical Engineering

Program

Biomedical Engineering

Advisor

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DOI

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