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Histopathology plays a vital role in cancer diagnosis, prognosis, and treatment decisions. The whole slide imaging technique that captures the entire slide as a digital image allows the pathologists to view the slides digitally as opposed to what was traditionally viewed under a microscope. The recent development of deep learning methods has resulted in powerful computational methods for the quantitative and objective analyses of histopathology images, which can reduce the intensive labor and improve the efficiency of pathologists compared with manual examinations. In this talk we focus on deep learning based solutions for histopathology image analysis for cancer diagnosis and treatment, specifically, nuclei segmentation for cancer diagnosis, gene mutation and pathway activity prediction for cancer treatment. We will outline our computational methods for annotation efficient weakly supervised and instance segmentation nuclei segmentation algorithms with 60 times speed up. We will also present a deep learning model to predict the genetic mutations and biological pathway activities directly from histopathology slides in breast cancer. The weight maps of tumor tiles are visualized to understand the decision-making process of deep learning models. Our results provide new insights into the association between pathological image features, molecular outcomes and targeted therapies for breast cancer patients.
Dimitris Metaxas is a Distinguished Professor in the Computer and Information Sciences Department at Rutgers University. He is directing the Center for Computational Biomedicine, Imaging and Modeling (CBIM) and the NSF University-Industry Collaboration Center CARTA with emphasis on real time and scalable data analytics, AI and machine learning methods with applications to computational biomedicine and computer vision. Dr. Metaxas has been conducting research towards the development of methods and technology upon which AI, machine learning, computer vision, medical image analysis, and computer graphics can advance synergistically. In medical and biological image analysis new AI, Machine Learning and model-based methods have been developed for material modeling and shape estimation of internal body parts (e.g., heart, lungs) from MRI, SPAMM and CT data, cancer diagnosis, cell segmentation from histopathology images, cell tracking, cell type analysis and linking genetic mutations to cells. Dr. Metaxas has published over 700 research articles in these areas and has graduated over 64 PhD students, who occupy prestiguous academic and industry positions. His research has been funded by NIH, NSF, AFOSR, ARO, DARPA, HSARPA, and the ONR. Dr. Metaxas work has received many best paper awards and he has 8 patents. He was awarded a Fulbright Fellowship in 1986, is a recipient of an NSF Research Initiation and Career awards, and an ONR YIP. He is a Fellow of the American Institute of Medical and Biological Engineers, a Fellow of IEEE and a Fellow of the MICCAI Society. He has been general chair of IEEE CVPR 2014, Program Chair of ICCV 2007, General Chair of ICCV 2011, FIMH 20011 and MICCAI 2008 and the Senior Program Chair for SCA 2007.
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