Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment (Volume II)

Dear Colleagues,

This Special Issue is the second edition of a previous one, entitled “Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment”.

Deep learning is a machine learning method that allows for computational models composed of multiple processing layers to be fed raw data and automatically learn various abstract data representations for detection and classification. New deep learning methods and applications are demanded with advances in medical imaging. Due to considerable variation and complexity, it is necessary to learn the representations of clinical knowledge from big imaging data for a better understanding of health informatics. However, numerous challenges include diverse and inhomogeneous inputs, high-dimensional features versus inadequate subjects, subtle key patterns hidden by sizeable individual variation and, sometimes, an unknown mechanism underlying the disease. These challenges and opportunities are inspiring, and an increasing number of people are devoted to the research direction of machine learning in medical imaging nowadays.

The sudden increase in the market for imaging science enhances opportunities to develop new imaging techniques for diagnosis and therapy. New clinical indications using proteomic or genomic expression in oncology, cardiology and neurology also offer a stimulus. This promotes the growth of procedure volume and sales of clinic imaging agents, and the development of new radiopharmaceuticals. This Special Issue will bring together researchers from diverse fields and specializations, such as healthcare engineering, bioinformatics, medical doctors, computer engineering, computer science, information technology and mathematics.

Potential topics include, but are not limited to:

• Recent advances in bioimaging applications in preclinical drug discovery;
• Computer-aided detection and diagnosis;
• Image analysis of anatomical structures/functions and lesions;
• Multi-modality fusion for analysis, diagnosis, and intervention;
• Deep learning for medical applications;
• Automated medical diagnostics;
• Advances in imaging instrumentation development;
• Hybrid imaging modalities in disease management;
• Medical image reconstruction;
• Medical image retrieval;
• Molecular/pathologic/cellular image analysis;
• Dynamic, functional and physiologic imaging.

Prof. Dr. Marcin Woźniak
Prof. Dr. Muhammad Fazal Ijaz
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. Cancers 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 2900 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
• machine learning
• medical imaging
• bioimaging
• computer-aided diagnosis oncology

• Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment in Cancers (20 articles)