Aims and Scope
Imaging is a cornerstone of medicine. The number of medical imaging studies is rapidly growing, and so is the size and dimensionality of these images. Human experts interpret these images, which is time-consuming, expensive, and error prone because of visual fatigue. Recent advances in deep learning show that computers can extract more information from images, more reliably, and more accurately than ever before. However, most deep learning research in computer vision has focused on natural images. Adapting and further developing these techniques to the characteristics of medical images and medical data is an important and relevant research challenge.
Many conferences cover either medical imaging or machine learning. Although many of them do cover the application of deep learning to medical imaging, most explicitly through satellite events like workshops, there is currently no single venue which brings deep learning and medical imaging researchers together for in-depth discussion and exchange of ideas. With hundreds of deep learning papers being published in the field of medical imaging, and numerous AI-based startups in the medical field appearing, we believe such a venue is needed.
This conference will be a forum for deep learning researchers, clinicians and health-care companies to take a leap in the application of deep learning based automatic image analysis in disease screening, diagnosis, prognosis, treatment selection and treatment monitoring. The conference will have a broad scope and include topics such as computer-aided screening and diagnosis, detection, segmentation, (multi-modal) registration, image reconstruction and synthesis. Furthermore, we discuss issues regarding the lack of curated, annotated datasets, noisy reference standards and the high-dimensionality of medical data. Software demonstrations, presentation of medical data sets and innovative clinical applications are also covered as focus points for integration of deep learning algorithms in clinical practice.
The 3-day program will include keynote presentations from invited speakers, oral presentations, posters, and live demonstrations of deep learning algorithms (academia and industry).