Yassine Ben Ayed
Higher Institute of Computer Sciences and Multimedia of Sfax
MIRACL Multimedia, InfoRmation Systems and Advanced Computing Laboratory
University of Sfax, Tunisia
E-mail: yassine.benayed@isims.usf.tn


Wael Ouarda
Digital Research Center of Sfax,
Ministry of Higher Education and Scientific Research, Tunisia
E-mail: wael.ouarda@crns.rnrt.tn

Machine learning has gained a lot of popularity and acceptance in recent years. We are witnessing a rapid digital transformation and the adoption of disruptive technology across different industries. Healthcare was one of the potential sectors that gained many benefits from deploying disruptive technologies. Machine learning, and deep learning have become an imperative part of the sector. They have a huge impact in healthcare and they have enabled the sector to improve patient monitoring and diagnostics. In addition, the use of multimodal information with the several machine learning types has empowered healthcare and many advacements were achieved not only in supervised and unsupervised learning, but also by using semi-supervised and reinforcement learning methods. Other approaches are especially very helpful in healthcare research such as domain adaptation, transfer learning, generative and adversarial learning, etc. This Special Session on Innovative use of Machine Learning and Deep Learning for Healthcare Empowerment (Innov-Healthcare 2022) at the 14th International Conference on Computational Collective Intelligence (ICCCI 2022) aims to bring together two usually insular disciplines: computer scientists with artificial intelligence, machine learning, and deep learning expertise, and clinicians/medical researchers.
Submissions offering proof studies representing clinical relevance and original applications are encouraged as much as novel methodological contributions. The Innov-Healthcare 2022 topics of interest deal with, but are not limited to, the use of different types of machine learning methods for:

• Health monitoring based on face, gesture, body analysis;
• Depression and Anxiety analysis based on visual features;
• Physiological measurement employing face analytics;
• Databases on health monitoring;
• Healthcare at home;
• Cognition based technology;
• Emotional hearing and understanding;
• Privacy preserving systems;
• Quality of life based technologies;
• Applications for the visually impaired;
• Sign language recognition and applications for hearing impaired;
• Egocentric and first-person vision;
• Assessing physical and/or cognitive ability based on face and body movement analysis;
• Applications to improve health and wellbeing of children and elderly;
• Neurodegenerative pathology diagnosis and monitoring;
• Medical Images Segmentation, Analysis and Representation, etc.