Quality Assessment for Health Applications (QAH)
- Assemble all the existing publicly accessible databases on medical quality
- Develop databases with new diagnostic tasks and new objective quality assessment models
- Provide methodologies, recommendations, and guidelines for subjective test of medical image quality assessment
- Study the quality requirements and Quality of Experience (QoE) in the context of telemedicine and other telehealth services
Task-based approaches have been popular in the medical image quality assessment domain since 1993, named “model observer.” The underlying paradigm is to quantify the quality of a particular image by its effectiveness with respect to its intended task. A system or algorithm that enables medical experts to gain a better diagnostic task performance or to spend less time for interpretation with the same diagnostic reliability is said to be better.
Because of the methodology limitations, only detection and localization tasks have been addressed in the literature. Nowadays, with the development of deep learning and image-text retrieval technologies, the characterization task may be modeled. Thus in this project, we want to promote the task-based approaches (make a bridge between VQEG and medical image quality community) for medical images and explore this diagnostic task.
In addition, since the COVID-19 pandemic, the applications of multimedia systems in e-health becomes critical, in particular in the area of telemedicine. Hence, the medical image quality assessment becomes extremely important in many applications (e.g. verifying the acceptable quality when accelerating the CT scan and evaluating quality for tele-consultation, tele-expertise, and other telehealth services).
Questions should be addressed to the QAH Chair Lu Zhang (firstname.lastname@example.org), Vice Chair Meriem Outtas (Meriem.Outtas@insa-rennes.fr), and Vice Chair Lucie Lévêque (email@example.com).