Machine Learning in Medical Applications
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 (30 December 2020) | Viewed by 53424
Special Issue Editors
Interests: machine learning (artificial intelligence); mobile robots; robot vision; visual servoing; motion control; representation learning; path planning; position control; autonomous aerial vehicles
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent robots; decision support systems; artificial intelligence; multi-agent systems; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Healthcare is an important industry which offers value-based care to millions of people while, at the same time, being a top revenue earner for many countries. Machine learning (ML) in healthcare, medical diagnosis, and treatment is one such area that is seeing gradual acceptance in the industry. Google recently developed a machine-learning algorithm to identify cancerous tumors in mammograms, and researchers at Stanford University are applying deep learning to detecting skin cancer. Machine learning has already been helpful in a variety of situations in healthcare. ML in healthcare helps to analyze thousands of different datapoints and suggest outcomes, provide timely risk scores, and has many other applications. Therefore, the increasingly growing number of applications of machine learning in healthcare allows us a glimpse into a future where data, analysis, and innovation work hand-in-hand to help countless patients. Soon, it will be quite common to find ML-based applications embedded with real-time patient data available from different healthcare systems in multiple countries, thereby increasing the efficacy of new treatment options that were previously unavailable.
This particular collection aims to bring forward recent advances and present state-of-the-art developments in the theoretical and practical aspects of machine learning in healthcare. Since the emergence of deep-learning techniques and advanced computation technologies, many researchers of different backgrounds have contributed to this area, which has benefited from the heterogeneity and interdisciplinary of finding that are now well established. Much has been achieved; however, many challenges still lie ahead. Thus, this Special Issues serves as an essential and timely update on this topic and should be of interest to potential readers. We anticipate attracting high-quality papers that can fully reflect the progress in processing diagnostic information for healthcare. We specifically target contributions focused on novel learning mechanisms and their applications in medicine. Interdisciplinary contributions to this Special Issue will include but are not be limited to the following areas:
- Validation, analysis, and learning of data representation for medical imaging diagnosis.
- Theoretical or methodological developments in machine learning for personalized medicine.
- The applications of machine learning in radiotherapy, chemotherapy, endoscopic images, laryngoscopic images, MRI, CT imaging, etc.
- Acute treatments or diagnoses for specific clinical domains.
Prof. Dr. Kao-Shing Hwang
Guest Editors
Dr. Haobin Shi
Co-Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- deep learning
- medical imaging
- image detection/classification
- image segmentation
- intelligent healthcare
- cancer prevention
- personalized medicine
- improving technology
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