Revolutionizing Medical Image Analysis with Deep Learning, 2nd Edition

Dear Colleagues,

This Special Issue of the MDPI journal Electronics, titled "Revolutionizing Medical Image Analysis with Deep Learning", focuses on the growing trend in 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.

In addition to deep learning, the integration of Explainable Artificial Intelligence (XAI) is emerging as a critical area of focus. XAI aims to make the decision-making processes of complex deep learning models more transparent and interpretable, which is particularly crucial in the sensitive domain of medical imaging. As the adoption of AI in healthcare increases, explainability becomes a key factor in gaining the trust of clinicians and patients and ensuring the ethical use of AI in critical health decisions.

The goal of this Special Issue is to bring together recent advances and cutting-edge research in the use of both deep learning and XAI 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 addition of explainability considerations will not only address technical aspects but also delve into the implications for clinical workflows, regulatory standards, and patient safety.

The scope of the Special Issue is interdisciplinary, bringing together experts from various fields such as computer science, engineering, medicine, biology, and ethics. 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, trends, and the growing need for interpretability in AI-driven models.

The focus of the Special Issue is on the practical applications, novel approaches, and explainability 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;
• Explainable AI techniques applied to medical image analysis to enhance interpretability and transparency;
• Model explainability and trustworthiness in AI-driven medical applications;
• Regulatory considerations and challenges in the deployment of XAI in healthcare;
• Novel datasets, challenges, and benchmarks for application and evaluation of DL; annotation-efficient approaches to DL;
• Comprehensive surveys and reviews on medical image processing, analysis, and explainability.

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 and XAI in medical image analysis. The Issue is an excellent resource for researchers, clinicians, and practitioners interested in exploring the potential of deep learning and explainable AI for medical image analysis.

We invite submissions from researchers and practitioners in these exciting and rapidly growing fields as we work towards revolutionizing medical image analysis and ensuring that deep learning technologies are both powerful and understandable in their application to healthcare.

Dr. Marija Habijan
Dr. Irena Galić
Dr. Daniel Vasić
Dr. Mirela Kundid Vasić
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. 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.

• medical image analysis
• image processing
• image classification
• image segmentation
• image registration
• image reconstruction
• X-rays
• CT scans
• MRI
• PET
• ultrasound
• computer-aided diagnosis
• computer-aided treatment planning
• artificial intelligence
• deep learning
• machine learning
• neural networks
• explainable artificial intelligence (XAI)
• model interpretability
• transparency in AI

• Revolutionizing Medical Image Analysis with Deep Learning in Electronics (12 articles)