AIMIA

Abstract: The ExaMode project addresses several challenges that particularly deep learning has when using histopathology data. From creating data sets for concrete scenarios, using weak labels as annotations and multimodal learning from the data in collaboration with hospitals, the project really chooses a practical approach for machine learning-based decision support in digital pathology.

About Henning Müller: Henning Müller studied medical informatics at the University of Heidelberg, Germany, then worked at Daimler-Benz research in Portland, OR, USA. From 1998-2002 he worked on his PhD degree in computer vision at the University of Geneva, Switzerland with a research stay at Monash University, Melbourne, Australia. Since 2002, Henning has been working for the medical informatics service at the University Hospital of Geneva. Since 2007, he has been a full professor at the HES-SO Valais and since 2011 he is responsible for the eHealth unit of the school. Since 2014, he is also professor at the medical faculty of the University of Geneva. In 2015/2016 he was on sabbatical at the Martinos Center, part of Harvard Medical School in Boston, MA, USA to focus on research activities. Henning is coordinator of the ExaMode EU project and was coordinator of the Khresmoi EU project, scientific coordinator of the VISCERAL EU project. Since early 2020 he is also a member of the Swiss National Research Council.

Abstract: Some have called it the 3rd revolution in pathology: digitization and AI. On the one hand, digitization of histopathology images opens the window of opportunity for new diagnostics tools, innovative technologies for research and bridging the gap between different medical domains, but on the other is associated with the challenges of handling huge amounts of data, the lack of standardization and an insecurity around the implementation of AI. How will this revolution affect personalized medicine and what is pathology of the future?

About Inti Zlobec: Inti Zlobec is Professor of Digital Pathology (Extraordinarius) at the Institute of Pathology, University of Bern, Switzerland. Originally from Montreal, Canada, she graduated with a PhD degree in Experimental Pathology, from McGill University before moving to Switzerland in 2007. She received her Habilitation in 2010 after a post-doctoral fellowship at the University Hospital Basel, where she conducted tissue-based research including molecular pathology in the field of colorectal cancer using biostatistical models. Now, she leads an inter-disciplinary research group using artificial intelligence to study pathology data and images. The aim is to discover and validate novel prognostic and predictive biomarkers for cancer patients, and their potential implementation into diagnostic routine. Inti Zlobec is a member of the Executive Team of the Center for Artificial Intelligence in Medicine (CAIM) of the University of Bern, Co-Founder and President of the Swiss Consortium for Digital Pathology (SDiPath) and Chair of the European Society of Pathology (ESP) Working Group IT (Computational).

Abstract: 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.

About Dimitris Metaxas: 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 novel 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 63 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.