Medical Image Analysis and Machine Learning

Dear Colleagues,

Medical image analysis, when combined with machine learning, has transformed the landscape of diagnostic medicine by enabling more accurate, efficient, and early disease detection. Despite the growing prevalence of machine learning applications in medicine, there remains a significant gap in translating algorithmic advances into clinically viable tools that can assist healthcare professionals across various specialties. This Special Issue of Diagnostics aims to attract cutting-edge contributions that demonstrate novel methodologies, robust validation frameworks, and real-world applications of machine learning and deep learning in medical image interpretation.

Medical images—ranging from radiological scans (MRI, CT, PET) to microscopic histopathological slides and digital retinal images—contain complex patterns and hidden features that require advanced analytical tools to decode. Machine learning has shown considerable promise in automating feature extraction, enhancing diagnostic accuracy, enabling early disease detection, and predicting treatment response. However, the success of such tools is dependent on high-quality data, interpretable models, cross-domain collaborations, and rigorous benchmarking against clinical standards.

This Special Issue will explore all aspects of image-based diagnostics, including classical computer vision techniques, convolutional neural networks (CNNs), transformers, radiomics, and multimodal fusion strategies. We also welcome research focusing on longitudinal analysis, transfer learning for low-resource settings, privacy-preserving models, explainable AI (XAI), and the integration of imaging biomarkers with genomics or electronic health records. A particular emphasis will be placed on frameworks that enable reproducibility, generalizability, and ethical use of AI in healthcare.

We invite original research articles, comprehensive reviews, and relevant clinical case studies that provide deep insights into state-of-the-art machine learning techniques tailored for medical image analysis. Submissions with translational potential and interdisciplinary collaboration between clinicians, computer scientists, and bioengineers are especially encouraged.

Dr. Inzamam Nasir
Prof. Dr. Costin Teodor Streba
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. 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.

• AI-enhanced clinical imaging workflows
• deep learning for disease classification and segmentation
• radiomics and multi-omics integration
• federated learning and privacy-preserving analytics
• next-generation medical image annotation techniques
• explainable AI in diagnostic imaging
• high-throughput screening in digital pathology
• cross-domain adaptation and transfer learning in healthcare
• prognostic and predictive imaging biomarkers
• clinical decision support systems using machine learning