Equitable Deep Learning For Regulatory Science And Clinical Research For Improving Health Outcomes In Patients

This session is suitable for ISBI community members interested in modern topics in acquisition of medical images and their real-world clinical applications in computer vision, deep learning, regulatory science and challenges such as bias and inequity and emerging regulatory framework. Topics include digital image processing, deep learning, tissue structure and anatomy, uncertainty estimations and statistical modeling. In addition, newer concepts in using regulatory science methods for Software as a Medical Device (SaMD, and equity and fairness for benchmarking data and models for medical imaging will also be covered. While few of these topics have been previously offered at ISBI, we did not find a consolidated session bringing together diverse communities of clinical imaging, deep learning, regulatory science and fairness. We propose that these topics will be of significant interest to the biomedical imaging researchers and industry, and speakers are from communities that do not traditionally attend ISBI. This session will also focus on diversity and inclusion of patients and data often under-represented in medical imaging research.