Revolutionizing Medical Image Analysis with Deep Learning

Dear Colleagues,

This Special Issue of the MDPI journal Electronics, titled "Revolutionizing Medical Image Analysis with Deep Learning," focuses on the growing trend of using deep learning algorithms in the field of medical imaging. Medical imaging is an essential component in the diagnosis, treatment, and monitoring of various diseases and conditions, and deep learning has the potential to significantly improve the accuracy, efficiency, and reliability of medical image analysis.

The goal of this Special Issue is to bring together recent advances and cutting-edge research in the use of deep learning in medical image analysis. The Issue also aims to provide a comprehensive overview of the current state-of-the-art and to highlight the challenges, opportunities, and future directions of this rapidly evolving field.

The scope of the Special Issue is interdisciplinary, bringing together experts from various fields, such as computer science, engineering, medicine, and biology. The Issue is designed to be a useful resource for researchers, clinicians, and practitioners in the field of medical imaging, and to provide them with valuable insights into the latest developments and trends in deep learning.

The focus of the Special Issue is on the practical applications and novel approaches of deep learning in medical image analysis, including but not limited to:

• Novel applications of deep learning (DL) in medical image processing and analysis;
• DL approaches for medical image segmentation and classification (X-rays, CT, MRI, PET, ultrasound);
• DL approaches for medical image registration, super-resolution, and resampling;
• Un/semi/weakly-supervised learning for medical image processing and analysis;
• Domain adaptation, transfer learning, and adversarial learning in medical imaging with DL;
• Multi-modal medical imaging data fusion and integration with DL;
• Joint latent space learning with DL for medical imaging and non-imaging data integration;
• Spatiotemporal medical imaging and image analysis using DL;
• Novel datasets, challenges, and benchmarks for application and evaluation of DL, annotation efficient approaches to DL;
• Comprehensive surveys and reviews on medical image processing and analysis.

This Special Issue provides a valuable supplement to the existing literature in the field by bringing together a wide range of perspectives on the use of deep learning in medical image analysis. The Issue is an excellent resource for researchers, clinicians, and practitioners interested in exploring the potential of deep learning for medical image analysis.

Dr. Irena Galić
Dr. Marija Habijan
Dr. Antonio Lanata
Dr. Luca Mesin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.