Deep Learning in Medical Image Segmentation and Diagnosis

Dear Colleagues,

Deep learning has achieved tremendous growth in the solution of different signal processing, pattern recognition, natural language, forecasting, image and speech recognition, medical, healthcare, computer vision, and data science applications. Deep learning techniques can efficiently process a huge number of data, automatically extract useful features from data sources and visual images, learn temporal relationships between data items of time series, transfer knowledge from one system to another, and solve problems characterized with high-order nonlinearities. Various emerging directions in deep learning— attention mechanisms, transformers, generative adversarial networks, and residual networks—have attracted attention for their potential in solving different problems. Image segmentation and diagnosing is one of the active problems in medicine. The availability of sufficient data and images from medical devices has positively affected the development of computer-aided systems for segmentation, diagnosis, and analysis of diseases. Recently, these systems using MRI, CT, and X-ray images have also attracted great interest for medical investigation. Deep learning is one of the emerging approaches that can use these data and images and identify their effective solutions. Because of its high computational power, deep learning techniques can be efficiently used in medical diagnosis and medical image processing.

The goal of the given SI is to review the research articles about the application of emerging trends of deep learning techniques for solving medical diagnostic problems.

Potential topics include but are not limited:

• Deep learning in medical diagnosis;
• Medical imaging and deep learning;
• Data mining and deep learning;
• Emerging deep learning techniques and diagnosis;
• Deep learning for the classification of lesions and disease;
• Deep learning for segmentation, denoising, and super-resolution;
• Deep learning and healthcare;
• Deep learning for signal analysis;
• Learning mechanisms in deep neural networks;
• Ensemble learning;
• Stacked networks;
• Medical informatics;
• Computer-assisted diagnosis.

The authors are required to read guidelines for the preparation of research papers. Prospectus authors should submit their manuscripts through manuscript submission systems at https://www.mdpi.com/journal/diagnostics/sections.

Prof. Dr. Rahib Abiyev
Guest Editor

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. Diagnostics 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 2600 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.

• deep learning in medical diagnosis
• medical imaging and deep learning
• data mining and deep learning
• emerging deep learning techniques and diagnosis
• deep learning for the classification of lesions and disease
• deep learning for segmentation, denoising, and super resolution
• deep learning for signal analysis
• learning mechanisms in deep neural networks
• ensemble learning
• stacked networks
• medical informatics
• computer-assisted diagnosis