Deep Learning in Medical Image Analysis: Challenges and Applications

Gobert Lee (Curatore)

Hiroshi Fujita (Curatore)

Springer Nature Switzerland AG, 2021

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.

• Curatore: Gobert Lee, Hiroshi Fujita
• Editore: Springer Nature Switzerland AG
• Collana: Advances in Experimental Medicine and Biology
• Anno: 2021
• Rilegatura: Paperback / softback
• Pagine: 181 p.
• Dimensioni: 254 x 178 mm
• EAN: 9783030331306

Testo in English