Machine Learning Techniques to Diagnose Breast Cancer
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".
Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 3404
Special Issue Editors
Interests: computer-aided diagnosis; medical informatics; medical image processing; human brain connectome; big data management and analytic; blockchain technology
Interests: computer-aided diagnosis; medical informatics; medical image processing; human brain connectome
Special Issue Information
Dear Colleagues,
Machine learning, especially artificial intelligence, has significant applications in the detection and diagnosis of breast cancer. Radiomics and deep learning utilize a combination of huge amounts of multi-modal data and better calculation methods to motivate the development of analytical techniques of medical figures of breast cancer. Machine learning will affect all perspectives of the course of medication of breast cancer patients based on images, including detection, diagnosis, prognosis, evaluation of therapeutic reaction, risk analysis, etc. In current researches, the main method used is the open-source artificial intelligence method. The open-source structure and the training weights provide convenience for the artificial intelligence methods to be used upon data of breast cancer. However, many elements prevent it from clinical application experiment, including a lack of explanation, standardization, reproducibility, and generalizability, among other things. As a result, the Special Issue ’Machine Learning Techniques to Diagnose Breast Cancer‘ aims to illustrate the development of machine learning methods in clinical breast cancer and the new applications involved in all processes of diagnosis and therapy of breast disease patients from a combination of radiomics and machine learning methods. It also seeks to provide a reference for clinical application for the researchers.
Topics of interest for this Special Issue include, but are not limited to, the following:
- Machine learning for breast cancer diagnostic;
- Machine learning for breast cancer detection;
- Automatic breast cancer image pre-processing;
- Breast cancer image segmentation;
- Prognostic prediction methods for breast cancer;
- Breast cancer risk assessment.
- Interpretable and explanation CAD models for breast cancer;
- Other machine models for any cancer.
Prof. Dr. Junchang Xin
Dr. Zhongyang Wang
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- machine learning
- CAD
- interpretation
- clinical application