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Clinical diagnosis based on medical imaging often involves multiple imaging modalities, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), Ultrasound, and other clinical data such as Electronic Health Records (EHR), laboratory test results, and genetic data. Each modality provides complementary perspectives on a patient’s physiological or pathological conditions, and the combination of these diverse data sources holds the potential to offer a more comprehensive understanding of the patient’s health status.

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Multimodal Deep Learning for Medical Image Analysis

  • 章节

  • 发表日期：

    2026年

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  • Yen-Wei Chen,
  • Lanfen Lin,
  • Rahul Kumar Jain

摘要

Clinical diagnosis based on medical imaging often involves multiple imaging modalities, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), Ultrasound, and other clinical data such as Electronic Health Records (EHR), laboratory test results, and genetic data. Each modality provides complementary perspectives on a patient’s physiological or pathological conditions, and the combination of these diverse data sources holds the potential to offer a more comprehensive understanding of the patient’s health status.