Conferences
Dr. Yelena Yesha will be a featured keynote speaker at Vanderbilt University for MIDL 2023 (Medical Imaging with Deep Learning) being held in Nashville, Tennessee on July 10-12, 2023. Read more “Dr. Yelena Yesha Keynotes Day 2 of MIDL 2023 Conference 7/11”
Publications
University of Utrecht researcher Itir Onal Ertugrul, University of Miami researchers Yeojin Amy Ahn and Daniel Messinger along with others published a study on automated facial action recognition in infants – which focuses on training Action Unit detectors.
Read more “Infant AFAR: Automated facial action recognition in infants”
Publications
Daniel Messinger, Lynn Perry, et al. contributed to the book series “Advances in Child Development Behavior,” Volume 62 “New Methods and Approaches for Studying Child Development,” Chapter 7 “Computational Approaches to Understanding Interaction and Development,” which focuses on vocal interaction and development in children.
Read more “Computational Approaches to Understanding Interaction and Development”
Publications
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and restricted and repetitive patterns of behavior. Autism is estimated to affect 1 in 59 children in the United States and costs roughly $35 billion dollars to the society. Read more “Deep Learning Based Multimedia Data Mining for Autism Spectrum Disorder (ASD) Diagnosis”
Publications
Symmetry is a mathematical concept only partially explored in networks, especially at the applicative level. One reason is a certain lack of interpretable inference obtained from networks. While the network systemic associations (links) between entities (nodes) emerge from the underlying dependence structure, this latter is only partially explicit via the established direct interactors and remains to a certain extent latent (distant node predicted paths). Read more “Inference From Complex Networks: Role of Symmetry and Applicability to Images”
Publications
In past years, medical oncology has witnessed an unprecedented explosion in the understanding of cancer pathophysiology and pathogenesis. With the advancement of next-generation sequencing technologies such as single-cell RNA sequencing, we are better equipped to explore and model complex phenomena such as cancer heterogeneity, resistance, and etiologies at a granular level. Read more “Machine and Deep Learning Approaches for Cancer Drug Repurposing”
Events
Artificial intelligence (AI) has great potential to augment the clinician as a virtual radiology assistant (vRA) through enriching information and providing clinical decision support. Deep learning is a type of AI that has shown promise in performance for Computer Aided Diagnosis (CAD) tasks. A current barrier to implementing deep learning for clinical CAD tasks in radiology is that it requires a training set to be representative and as large as possible in order to generalize appropriately and achieve high accuracy predictions. Read more “Yelena Yesha Lecture 2/5/2020 on Medical Imaging”