Deep Learning in Medical Image Analysis for Personalized Medicine

• Published in: 2023 International Symposium ELMAR pp. 207 - 212
• Main Authors: Galic, Irena, Habijan, Marija
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.09.2023
• Subjects: Convolutional neural networks; Deep learning; Generative adversarial networks; Image registration; Image segmentation; Medical Image Analysis; Medical Image Classification; Medical Image Registration; Medical Image Segmentation; Precision medicine; Recurrent neural networks
• ISSN: 2835-3781
• DOI: 10.1109/ELMAR59410.2023.10253934

Deep learning (DL) has transformed the field of medical image processing, enabling unprecedented accuracy and efficiency in various applications. It has been widely utilized for medical image analysis of different anatomical regions. In this article, we provide an overview of commonly used deep learning methods, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). We briefly overview their applications in medical image analysis, such as image classification, object detection/localization, segmentation, generation, and registration. We also highlight the strengths and limitations of each method and identify the challenges that still need to be addressed, including the limited availability of annotated data, variability in medical images, and the interpretability issue. Finally, we discuss future research directions, including developing explainable deep learning methods and integrating multi-modal data.