Deep Learning for Medical Images: Challenges and Solutions

Dear Colleagues,

Recently, with the great success of artificial intelligence (AI) and deep learning (DL) in areas of natural images, adapting and further developing DL techniques to medical images is an important and relevant research challenge. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics, and healthcare in general. Medical images achieved by modalities including X-rays, magnetic resonance, microwaves, ultrasound, and optical methods have been used widely for clinical purposes. It is almost impossible for clinicians to diagnose diseases without the aid of medical images. However, interpreting these images manually is time-consuming, expensive, and varies depending on the clinician’s expertise. Advances in DL enable us to automatically extract more information from images more confidently than ever before. The techniques of AI and DL have played an important role in medical fields like medical image processing, computer-aided diagnosis, image interpretation, image fusion, image registration, image segmentation, etc.

This Special Issue calls for papers presenting novel works about medical image/video processing using DL and AI. Furthermore, high-quality review and survey papers are welcomed. The papers considered for possible publication may focus on, but not necessarily be limited to, the following areas:

• Deep learning and artificial intelligence for medical image/video;
• Image segmentation, registration, and fusion;
• Image processing and analysis;
• Image formation/reconstruction and image quality assessment;
• Medical image analysis;
• Computer aided diagnosis;
• Machine learning of big data in imaging;
• Integration of imaging with non-imaging biomarkers;
• Visualization in medical imaging.

Prof. Dr. Jitae Shin
Guest Editor

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