Artificial Intelligence in Screening Mammography: Recent Advances and Tools in Cancer Detection and Diagnosis
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 11673
Special Issue Editors
Interests: medical imaging; deep learning; breast cancer diagnosis
Special Issues, Collections and Topics in MDPI journals
Interests: medical image processing; breast cancer detection; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: medical imaging; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: medical imaging; deep learning; breast cancer diagnosis; robotics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Breast cancer is a major health issue and still a leading cause of fatality among women worldwide. Mammography remains the foremost effective procedure for the early detection and diagnosis of breast cancer. The aim of this Special Issue is to present the recent advances in the detection and diagnosis of cancerous regions in mammograms using machine learning and deep learning algorithms. We particularly welcome submissions that will utilize different mammography modalities (separately or in combination) such as digital mammography (DM), tomosynthesis, ultrasound or MRI in developing systems to assist the diagnosis (CADx) and/or the detection (CADe) of regions of suspicion in mammograms. Submissions can also include but are not limited to novel feature extraction techniques for breast cancer detection and diagnosis, transfer learning and deep learning architectures, open access databases for breast cancer research, generative adversarial network (GAN) architectures that overcome the problem of small data sets etc.
The intent of this Special Issue is to explore where we stand and what the future holds in this important health related research topic. To that end, we invite submissions involving new techniques, methods, applications, and results, as well as review articles.
Prof. Dr. Athanasios Koutras
Dr. Ioanna Christoyianni
Dr. George Apostolopoulos
Prof. Dr. Dermatas Evangelos
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. Applied Sciences 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
- digital mammography
- machine learning
- deep learning
- breast cancer
- ultrasound
- digital breast tomosynthesis