In recent years, Deep Learning (DL) application has shown promising results in conducting AI tasks, such as computer vision and speech recognition. Convolutional Neural Network (CNN) models have been utilized for identifying special objects targeted as preventive, detective, and diagnostic means in predictive medicine. However, automatic segmentation and classification of biomedical images have been a challenging task, especially for Magnetic Resonance Images (MRI) applied to diagnose and treatment of brain diseases. Moreover, it is a time-consuming procedure that requires trained biomedical experts to manually segment or annotate such MRI datasets. This talk will introduce Deep Learning (DL) approaches (Mask-RCNN and U-Net) which are the state-of-the-art convolutional neural network algorithms for object analysis in the biomedical domains. Mask-RCNN and U-Net have been applied to detection and segmentation of biomedical images obtained from specialized pathologic areas, such as Brain or Oral Diseases. In this presentation, we will demonstrate that DL based Mask-RCNN can be used to perform highly efficient automatic segmentation and classification of pathology imaging informatics.
Dr. Yugyung Lee is the Professor of Computer Science Electrical Engineering and Director of the UMKC Distributed Intelligent Computing (UDIC) Lab at the University of Missouri – Kansas City. Dr. Lee is currently a co-director of NSF IUCRC’s Center for Big Learning (CBL). Her research interests include Distributed Intelligent Computing and Systems, Real-time Big Data Analytics, Deep Learning, Semantic Web, Large Scale Software Systems, and Biomedical Applications. Dr. Lee has been involved in numerous projects funded by NSF, NIH, IBM, Toyota, and the Life Sciences Grant award from the Missouri Life Sciences Research Board, the Mid America Heart Institute, Children’s Mercy Hospital and the Heartlab Co. Dr. Lee has published over 120 refereed research papers. She has won the N.T. Veatch Award for Distinguished Research and Creative Activity in 2016, the IBM Smarter Planet Faculty Innovation Award from the IBM cooperation, the CS4HS Faculty Award from the Google Cooperation in 2015.