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Special Issue "Advances of Deep Learning in Medical Image Interpretation"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 30 June 2022.

Special Issue Editors

Dr. Zongwei Zhou
E-Mail Website
Guest Editor
Department of Computer Science. Johns Hopkins University, Baltimore, MD 21218, USA
Interests: medical image analysis; imaging informatics; computer vision
Prof. Dr. Tianming Liu
E-Mail Website
Guest Editor
Department of Computer Science, University of Georgia, Athens, GA 30602, USA
Interests: biomedical image analysis; biomedical informatics; signal/image processing

Special Issue Information

Dear Colleagues,

Deep learning has shown revolutionary progress in various aspects of medical image interpretation, propelling computer-aided diagnosis forward at a rapid pace. Deep learning excels at identifying and localizing intricate patterns from images and providing quantifiable assessments through image analysis. There is no doubt that the impact of deep learning on medical imaging will be tremendous. In the future, many medical images will reach physicians along with an interpretation provided by deep learning.

Medical images possess unique characteristics compared to photographic images, which provide both opportunities and challenges for applying deep learning to disease diagnosis and prognosis. Medical images contain quantitative imaging characteristics (e.g., the intensity scale and physical size of pixels) that can be used as valuable information to enhance deep learning performance. Medical images also present qualitative imaging characteristics (e.g., consistent and predictable anatomical structures with dimensional details) that can provide an excellent opportunity for algorithm development. Meanwhile, several characteristics unique to medical images create new challenges (e.g., isolated, discrepant data and partial, noisy labels) that must be addressed through additional investigation.

This Special Issue is to address significant challenges to deep learning adoption in medical image analysis. We are looking for methodological advancements in exploiting the unique characteristics of medical images, covering image modalities of radiology, cardiology, pathology, dermatology, etc.

Dr. Zongwei Zhou
Prof. Dr. Tianming Liu
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 papers will be 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. Sensors 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.

Keywords

  • applications of medical imaging
  • image segmentation, registration, and fusion
  • representation learning, feature extraction
  • image reconstruction, image enhancement
  • microscopy image analysis
  • machine learning, deep learning
  • computer-aided diagnosis
  • image-guided interventions and surgery

Published Papers

This special issue is now open for submission.
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