A review of AutoML optimization techniques for medical image applications

Area of Science:

• Medical Imaging Analysis
• Artificial Intelligence
• Machine Learning

Background:

• Machine learning and deep learning excel at analyzing large medical imaging datasets and detecting subtle patterns.
• Manual algorithm design and tuning for medical image analysis are time-consuming and challenging.
• Developing generalized models across diverse imaging modalities is difficult due to modality-specific characteristics.

Purpose of the Study:

• To provide an overview of Automatic Machine Learning (AutoML) approaches for medical image analysis.
• To categorize and analyze existing AutoML techniques, focusing on optimization search strategies.
• To discuss current challenges and future research directions in AutoML for medical imaging.

Main Methods:

• Comprehensive review and categorization of AutoML techniques for medical imaging.
• Analysis of optimization strategies, including evolutionary algorithms, gradient-based methods, and Bayesian optimization.
• Examination of specific AutoML components: Hyper-parameter Optimization (HPO), Neural Architecture Search (NAS), and Automatic Data Augmentation (ADA).

Main Results:

• AutoML techniques automate the pipeline for various medical image analysis tasks, addressing limitations of manual design.
• Different optimization techniques are crucial for the effectiveness of AutoML approaches.
• The study categorizes and analyzes various AutoML methods applied to medical imaging.

Conclusions:

• AutoML offers significant potential to streamline and enhance medical image analysis.
• Further research is needed to address current challenges and explore future directions in AutoML for medical AI.
• The integration of advanced optimization strategies is key to developing robust and generalized medical imaging AI.