Imaging Informatics: Computer-Aided Diagnosis

Dear Colleagues,

Imaging informatics has emerged as a subfield of biomedical informatics, studying how information about and contained within biomedical images is retrieved, analyzed, enhanced, and exchanged to improve the efficiency, accuracy, usability, and reliability of biomedical imaging. Benefiting from the availability of large amounts of biomedical images and the dramatic resurgence of artificial intelligence (AI), computer-aided diagnosis (CAD), one of the major applications of imaging informatics, has been immensely transformed and become more competent in supporting clinical decision making. Despite this remarkable progress, numerous challenges remain. For instance, compared with “enormous” photographic images in computer vision, the availability of biomedical image datasets is limited due to the need for expert annotation, privacy, and to follow regulatory requirements. Second, comprehensive clinical decision making requires not only images, but also free-form text (clinical reports), genomic, clinical data, and additional information still. It is challenging to integrate knowledge which are extracted from different modalities and incorporate prior human domain knowledge. Last but not least, most AI models are still considered black boxes. These are difficult to interpret and explain, and it is a challenge to learn from new data continually without forgetting prior knowledge, a problem which has largely hindered their clinical usage.

The aim of this Special Issue is to collate original research as well as review articles on the subject of imaging informatics, with special attention paid to computer-aided diagnosis.

Potential topics include but are not limited to the following:

• Development, evaluation, and deployment of computer-aided diagnosis;
• Annotation-efficient learning: self-supervised, semi-supervised, and unsupervised, low-shot learning;
• Multimodality integration;
• Interpretable and explainable AI;
• Continuous learning;
• Imaging technologies, including acquisition, reconstruction, normalization, standardization, enhancement, analysis, and feature extraction;
• Image classification, segmentation, recognition, detection, registration, and visualization;
• Education, policy, and clinical trials to advance computer-aided diagnosis research and clinical impact.

Dr. Ruibin Feng
Dr. Zongwei Zhou
Prof. Dr. Jianming Liang
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. Journal of Imaging 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 1800 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.

• computer-aided diagnosis
• artificial intelligence
• computer vision
• deep learning
• medical imaging analysis
• annotation-efficient learning