Advances in Medical Image Analysis and Deep Learning

Dear Colleagues,

With the development and progress of medical imaging and computer technology, medical image analysis has become an indispensable tool and technical means in medical research, clinical disease diagnosis and treatment. In recent years, deep learning (DL), especially deep convolutional neural networks (CNNs), has rapidly developed into a research hotspot in medical image analysis. These networks can automatically extract hidden features from medical image data related to diagnosis. Despite recent advances in DL research, questions remain on how best to learn representations of medical imaging data. Deep learning requires a large amount of data. However, the amount of patient data available for diseases is often insufficient, resulting in model promotion and optimization difficulties. The number of samples in disease categories is unbalanced, and the severe skewness of labels leads to poor generalizability of the model. Additionally, it is not enough to predict health or disease using solely medical image data. The exploration and fusion of other sources, types, or categories of data (signals, diagnostic reports, and other clinical parameters) is also very important, yet presents challenges for constructing DL models.

This Special Issue focuses on state-of-the-art DL techniques and their applications in medical imaging. We seek contributions that include, but are not limited to:

• Theoretical underpinnings of DL problems arising in medical imaging;
• Novel applications of DL in medical image acquisition, reconstruction, and analysis;
• Un/semi/weakly-supervised learning for DL; Annotation efficient approaches to DL;
• 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;
• DL approaches for medical image registration, super-resolution, and resampling;
• Accelerated medical imaging acquisition/reconstruction with non-Cartesian sampling using DL;
• Novel datasets, challenges, and benchmarks for application and evaluation of DL.

Authors must submit papers on https://susy.mdpi.com/ according to the instructions here. Three or four reviewers will typically be recruited according to the standard MDPI review protocol. Authors are encouraged to speak with one of the Guest Editors to determine the suitability for this Special Issue.

Dr. Jiquan Ma
Dr. Geng Chen
Dr. Hui Cui
Dr. Desi Shang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Information is an international peer-reviewed open access monthly 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 1600 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.

• medical image analysis
• deep learning
• AI healthcare
• deep neural networks
• convolutional neural networks