Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging.

BioMedInformatics Pub Date : 2025-06-01 Epub Date: 2025-04-14 DOI:10.3390/biomedinformatics5020020
Anh T Tran, Tal Zeevi, Seyedmehdi Payabvash
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引用次数: 0

Abstract

Artificial Intelligence (AI) and deep learning models have revolutionized diagnosis, prognostication, and treatment planning by extracting complex patterns from medical images, enabling more accurate, personalized, and timely clinical decisions. Despite its promise, challenges such as image heterogeneity across different centers, variability in acquisition protocols and scanners, and sensitivity to artifacts hinder the reliability and clinical integration of deep learning models. Addressing these issues is critical for ensuring accurate and practical AI-powered neuroimaging applications. We reviewed and summarized the strategies for improving the robustness and generalizability of deep learning models for the segmentation and classification of neuroimages. This review follows a structured protocol, comprehensively searching Google Scholar, PubMed, and Scopus for studies on neuroimaging, task-specific applications, and model attributes. Peer-reviewed, English-language studies on brain imaging were included. The extracted data were analyzed to evaluate the implementation and effectiveness of these techniques. The study identifies key strategies to enhance deep learning in neuroimaging, including regularization, data augmentation, transfer learning, and uncertainty estimation. These approaches address major challenges such as data variability and domain shifts, improving model robustness and ensuring consistent performance across diverse clinical settings. The technical strategies summarized in this review can enhance the robustness and generalizability of deep learning models for segmentation and classification to improve their reliability for real-world clinical practice.

提高神经影像学中深度学习分割分类鲁棒性和泛化性的策略。
人工智能(AI)和深度学习模型通过从医学图像中提取复杂模式,彻底改变了诊断、预测和治疗计划,实现了更准确、个性化和及时的临床决策。尽管前景广阔,但不同中心的图像异质性、采集协议和扫描仪的可变性以及对人工制品的敏感性等挑战阻碍了深度学习模型的可靠性和临床集成。解决这些问题对于确保人工智能驱动的神经成像应用的准确性和实用性至关重要。我们回顾和总结了提高神经图像分割和分类的深度学习模型的鲁棒性和泛化性的策略。本综述遵循一个结构化的协议,全面搜索谷歌Scholar、PubMed和Scopus关于神经成像、特定任务应用和模型属性的研究。同行评议的英语脑成像研究也被纳入其中。对提取的数据进行分析,以评估这些技术的实施和有效性。该研究确定了增强神经影像学深度学习的关键策略,包括正则化、数据增强、迁移学习和不确定性估计。这些方法解决了数据可变性和领域转移等主要挑战,提高了模型的鲁棒性,并确保了不同临床环境下的一致性能。本文总结的技术策略可以增强深度学习分割和分类模型的鲁棒性和泛化性,从而提高其在现实世界临床实践中的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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