Machine Learning and AI Methods in Computer Vision and Visualization for Medical and Healthcare Applications
A special issue of Computers (ISSN 2073-431X).
Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 298
Special Issue Editors
Interests: AI-related areas; data analytics, business intelligence; web technology and application; e-learning; mobile learning; human–computer interaction; virtual reality; augmented reality
Special Issue Information
Dear Colleagues,
Machine learning and relevant Artificial Intelligence (AI) methods have shown their capability and prevailed in many science and engineering fields including medicine and healthcare. Scientific visualisation in particular offers unrivalled methods for communicating various data elements. It broadly encompasses a number of significant areas of computer science research areas, such as visual computing, computer graphics, and computer vision. Machine learning algorithms are increasingly being used with visualisation and computer vision techniques as artificial intelligence advances. Because of the numerous non-intrusive, wearable, multi-modal, and sensor-based devices that are now possible thanks to technology, data gathered from healthcare-related activities and solutions serves as a good testing ground and playground for visualisations and computer vision techniques. These data typically bear certain distinctive qualities, such as having great practical value and being sensitive, complex, enormous in size, and multi-dimensional, which adds to the intrigue of the research investigation. The development of effective methods, applications, and even systems in healthcare is made possible by the integration of visualisation, computer vision, and machine learning.
Computer vision methods based on machine learning/AI have been developed for diagnosing tumours and nodules occurring in various human organs using image data acquired by different scanning modalities, e.g., CT and MRI. Some results are promising, but there is still room for improvement. It is often overlooked that machine learning/artificial intelligence techniques can be used for feature extraction and classification of non-image healthcare and medical data. These non-image data include text-based medical records, doctor's prescriptions, drug descriptions, diagnostic results, and more. This could be an important exploration of the possibilities of ML/AI. Visualization of non-image health data increases the utility of these data.
The Special Issue seeks to address the most recent developments in visualisation and computer vision employing machine learning and AI in the application area of medicine and healthcare, with a particular focus on healthcare data and its unsolved issues. The aim is to offer a thorough and up-to-date compilation of research and experiment efforts in the areas.
Dr. Pan Zheng
Dr. Shihua Zhou
Guest Editors
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Keywords
- visualization-based predictive analytics and therapy
- medical image processing and computer vision
- novel computer vision algorithms using healthcare data
- machine-learning-enriched visualization methods
- single and multi-dimensional medical image analysis
- visualization of e-health data
- cloud and big data visualization for healthcare
- clinical/patient record visualization and analytics
- patient behaviour data visualization and analysis
- visualization and machine learning in Assistive Technology
- visualization-aided diagnosis and prediction
- symptoms-related patterns detection and recognition
- visualization-guided medical procedures
- medical data (image and non-image) feature extraction
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