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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Continual learning in medical image analysis: A survey.

Xinyao Wu¹, Zhe Xu², Raymond Kai-Yu Tong¹

¹Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China.

Computers in Biology and Medicine

|September 27, 2024

Summary

Continual Learning (CL) in medical imaging enhances model adaptability and accuracy while mitigating catastrophic forgetting. This review details CL strategies for medical tasks, datasets, and future directions.

Keywords:

Catastrophic forgetting Class-incremental learning Continual learning Domain-incremental learning Survey Task-incremental learning

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Area of Science:

Medical Image Analysis
Artificial Intelligence
Machine Learning

Background:

Continual Learning (CL) is crucial for medical image analysis, addressing data privacy, adaptability, and accuracy challenges.
Catastrophic forgetting remains a primary obstacle in CL for medical applications.
Recent research highlights the growing significance and complexity of CL in the medical domain.

Purpose of the Study:

To provide an in-depth and up-to-date review of Continual Learning applications in medical image analysis.
To categorize existing CL methods into Task-Incremental, Class-Incremental, and Domain-Incremental Learning settings.
To correlate medical challenges with CL insights and assess method strengths/weaknesses.

Main Methods:

Categorization of CL methods into Task-, Class-, and Domain-Incremental Learning.
Subdivision based on representative learning strategies for diverse medical scenarios.
Analysis of strengths and weaknesses within specific medical contexts.

Main Results:

Comprehensive overview of CL strategies tailored for medical image analysis tasks.
Correlation established between medical challenges and CL solutions.
Assessment of commonly used benchmark datasets and evaluation metrics.

Conclusions:

CL offers significant potential for advancing medical image analysis, despite challenges like catastrophic forgetting.
Understanding CL settings and strategies is key to overcoming medical domain complexities.
Future research directions for CL in medical imaging are identified, building on current insights.