SS05: Applications of Machine Learning in Biomedicine and Healthcare
Miri Weiss Cohen
Braude College of Engineering, Israel
Daniele Regazzoni
University of Bergamo, Italy
Catalin Stoean
University of Craiova, Romania
Abstract
The use of Machine learning and deep learning in biomedicine and healthcare is increasingly being used to support clinical decision-making. A variety of approaches have been developed in recent years as biomedical and healthcare data and computational capabilities have grown rapidly. These approaches have been developed in order to address emerging problems through the use of these methods. Features can be automatically extracted, classification addressed, segmentation applied, and predictive models can be generated, allowing for more efficient analysis. Machine learning is being increasingly used in human modelling, pose assessments, and the analysis of pre- and post-clinical procedures, all in the context of healthcare.
The following topics are of particular interest, but are not limited to:
- Machine and deep learning for the diagnosis of medical conditions
- Machine and deep learning based management of medical data
- Machine learning based human modelling for healthcare
- Visualization and analysis of healthcare and clinical data
- Explainable Artificial Intelligence (XAI) for biomedicine and healthcare
- Machine and Deep learning applications in biomedicine, healthcare, and rehabilitation
Organizers
Dr. Miri Weiss Cohen is a professor at Braude College of Engineering in the Department of Software Engineering. Her research interests are in the area of Machine Learning (ML) methodologies as applied to engineering problems. In recent years, she has been focusing on two major topics: first, the prediction and optimization of green energy models (solar and wind) using big data time series. Secondly, the use of CT and MRI scans to identify cancer stages and reconstruct 3D volumes utilizing Deep Learning. She is collaborating with researchers in other disciplines related to machine learning in the context of rehabilitation, human-computer interfaces and design.
Dr. Daniele Regazzoni is a professor in the Department of Management, Information and Production Engineering at the University of Bergamo. He has been conducting research in the area of virtual and physical prototyping as part of the product development process. His research interests include methods and tools for product development and optimization, digital human modelling, reverse engineering, and additive manufacturing. He specializes in engineering technologies for health, including 3D scanning, virtual and augmented reality for rehabilitation, and additive manufacturing for products that are tailored to the needs of patients. He coordinates research activities within the ING-IND/15 scientific disciplinary sector.
Dr. Catalin Stoean has a research background in evolutionary computation and intelligent systems. His research interests involve finding appropriate machine learning means to solve real-world tasks from various fields like medicine, economy and even cultural heritage. The applications he works on refer to classification of data of various types (numerical, images, text), clustering, time series modelling, image segmentation. Deep learning represents another research topic where he has activated in recent years. Catalin is a Fulbright and DAAD alumni, he received the habilitation title in Romania in 2021, he published numerous articles in prestigious journals and presented the research findings at many international conferences and universities.