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Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in
Anh T Tran1, Tal Zeevi2, Seyedmehdi Payabvash1
1Department of Radiology, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, Columbia University, New York, NY 10032, USA.
Biomedinformatics
|April 24, 2025
Summary
Deep learning models enhance neuroimaging analysis but face challenges. Strategies like data augmentation and transfer learning improve their reliability for clinical use.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Deep Learning
Background:
- Artificial Intelligence (AI) and deep learning models offer advanced capabilities in medical image analysis for diagnosis and treatment.
- Challenges like data heterogeneity, varied acquisition protocols, and artifacts limit the reliability and clinical integration of these AI models in neuroimaging.
Purpose of the Study:
- To review and summarize strategies for enhancing the robustness and generalizability of deep learning models in neuroimaging.
- To address critical issues hindering accurate and practical AI-powered neuroimaging applications.
Main Methods:
- A structured literature search was conducted across Google Scholar, PubMed, and Scopus.
- Included peer-reviewed, English-language studies focused on neuroimaging, AI applications, and model attributes.
- Extracted data were analyzed to evaluate the implementation and effectiveness of various techniques.
Main Results:
- Key strategies identified include regularization, data augmentation, transfer learning, and uncertainty estimation.
- These techniques effectively address data variability and domain shifts, crucial for AI in neuroimaging.
- The identified strategies improve model robustness and ensure consistent performance across different clinical settings.
Conclusions:
- The reviewed technical strategies significantly enhance the robustness and generalizability of deep learning models for neuroimage segmentation and classification.
- Implementing these approaches is vital for improving the reliability of AI in real-world clinical neuroimaging practice.
