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Artificial Intelligence (AI), with a focus on machine learning (ML) and deep learning (DL), has become a cornerstone in the advancement of cardiac MRI analysis. This article explores the integration of supervised, unsupervised, and semi-supervised learning approaches, highlighting their distinct roles in addressing challenges like data annotation and achieving high diagnostic accuracy. Supervised learning methods excel in image segmentation and classification tasks, leveraging labeled datasets to detect structural and functional abnormalities. In contrast, unsupervised and semi-supervised paradigms uncover patterns in raw datasets and reduce the reliance on extensive manual labeling. Central to this discussion is the application of convolutional neural networks (CNNs) in cardiac MRI, enabling automated segmentation, disease classification, and functional parameter estimation. Architectural advancements such as U-Net models, attention mechanisms, and transfer learning are examined, demonstrating their potential to enhance model accuracy and efficiency while addressing challenges like dataset variability and imbalances. Data preprocessing techniques, including normalization, augmentation, and reconstruction, are presented as essential strategies for improving image quality and model generalizability. The present chapter also addresses key ethical and regulatory considerations, advocating for explainable AI and federated learning to ensure transparency, trust, and compliance in clinical applications. Emerging trends, such as 4D modeling, multimodal data integration, and real-time analysis, are discussed, showcasing the potential for further innovation in cardiac imaging. This comprehensive analysis underscores the transformative role of AI in cardiology, offering tools to improve diagnostic precision, streamline workflows, and enhance patient outcomes. By combining advanced computational techniques with clinical expertise, the adoption of AI-driven solutions in cardiac MRI holds the promise of a more efficient and accurate healthcare future.

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Deep Learning and Convolutional Neural Networks in Image Processing: Focusing on Cardiac MRI Analysis

  • 章节

  • 发表日期：

    2025年

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  • Amir Ghaffari Jolfayi,
  • Amirhossein Poopak,
  • Reza Elahi,
  • Mohammadhossein Mozafarybazargany,
  • Amir Azimi,
  • Seyed Amir Ahmad Safavi-Naini

摘要

Artificial Intelligence (AI), with a focus on machine learning (ML) and deep learning (DL), has become a cornerstone in the advancement of cardiac MRI analysis. This article explores the integration of supervised, unsupervised, and semi-supervised learning approaches, highlighting their distinct roles in addressing challenges like data annotation and achieving high diagnostic accuracy. Supervised learning methods excel in image segmentation and classification tasks, leveraging labeled datasets to detect structural and functional abnormalities. In contrast, unsupervised and semi-supervised paradigms uncover patterns in raw datasets and reduce the reliance on extensive manual labeling. Central to this discussion is the application of convolutional neural networks (CNNs) in cardiac MRI, enabling automated segmentation, disease classification, and functional parameter estimation. Architectural advancements such as U-Net models, attention mechanisms, and transfer learning are examined, demonstrating their potential to enhance model accuracy and efficiency while addressing challenges like dataset variability and imbalances. Data preprocessing techniques, including normalization, augmentation, and reconstruction, are presented as essential strategies for improving image quality and model generalizability. The present chapter also addresses key ethical and regulatory considerations, advocating for explainable AI and federated learning to ensure transparency, trust, and compliance in clinical applications. Emerging trends, such as 4D modeling, multimodal data integration, and real-time analysis, are discussed, showcasing the potential for further innovation in cardiac imaging. This comprehensive analysis underscores the transformative role of AI in cardiology, offering tools to improve diagnostic precision, streamline workflows, and enhance patient outcomes. By combining advanced computational techniques with clinical expertise, the adoption of AI-driven solutions in cardiac MRI holds the promise of a more efficient and accurate healthcare future.