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 15979
Special Issue Editors
Interests: digital sound processing and analysis; digital image processing and analysis; blind source and speech separation; biomedical signal processing and analysis; computer aided diagnosis systems; EEG/MEG brain signal analysis; brain computer interfaces; pattern recognition; machine learning; deep learning, artificial neural networks; music information retrieval; emotion recognition; time-series forecasting
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
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Keywords
- digital mammography
- machine learning
- deep learning
- breast cancer
- ultrasound
- digital breast tomosynthesis
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