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  • Book
    edited by Gobert Lee, Hiroshi Fujita.
    Summary: This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.

    Deep Learning in Medical Image Analysis
    Medical Image Synthesis via Deep Learning
    Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation
    Deep Learning Computer Aided Diagnosis for Breast Lesion in Digital Mammogram
    Decision support system for lung cancer using PET/CT and microscopic images
    Lesion Image Synthesis using DCGANs for Metastatic Liver Cancer Detection
    Retinopathy analysis based on deep convolution neural network
    Diagnosis of Glaucoma on retinal fundus images using deep learning: detection of nerve fiber layer defect and optic disc analysis
    Automatic segmentation of multiple organs on 3D CT images by using deep learning approaches
    Techniques and Applications in Skin OCT Analysis
    Deep Learning Technique for Musculoskeletal Analysis
    Digital Access Springer 2020