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AI Seminar: Neeraj Kumar, Computational Pathology – From Nuclei Segmentation to Precision Oncology

Postdoctoral Fellow and Amii researcher at the Greiner Lab (UAlberta) Neeraj Kumar presents "Computational Pathology – From Nuclei Segmentation to Precision Oncology" at the AI Seminar (July 10, 2020). The Artificial Intelligence (AI) Seminar is a weekly meeting at the University of Alberta where researchers interested in AI can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems, are explored. Bio: Neeraj Kumar just began as a postdoctoral fellow in the Greiner lab, University of Alberta. His research interests include computational pathology, medical image processing, machine learning for healthcare and medicine, and bioinformatics. Previously, he was a research associate at the center for computational imaging and personalized diagnostics at Case Western Reserve University, Cleveland. He completed his Ph.D. in Electronics and Electrical Engineering from the Indian Institute of Technology Guwahati, in 2017. After his Ph.D., he joined as a research fellow in cancer education and career development program of the department of pathology, University of Illinois at Chicago, to address clinically and biologically relevant cancer research questions by capitalizing on his expertise in image processing and machine learning. He was also a visiting researcher at the Institute of Research in Communications and Cybernetics at Ecole Centrale Nantes, Nantes, France, and at Beckman Institute of the University of Illinois at Urbana-Champaign, Champaign. He is a recipient of the numerous prestigious awards including the R25 trainee award from NCI (NIH, USA), Erasmus Mundus Heritage Fellowship, Microsoft Research India PhD fellowship, and the best poster award at University of Illinois Cancer Center’s annual research symposium. Abstract: With improvements in computer vision techniques and hardware, some of the problems of manual assessment of histology images, such as inter- and intra-observer variability, inability to assess subtle visual features, and the time taken to examine whole slides are being alleviated by computational pathology. A key module in several computational pathology pipelines is the one that segments nuclei. Accurate nuclei segmentation could facilitate downstream analysis of tissue samples for assessing not only cancer grades or stages but also for predicting tumor recurrence, treatment effectiveness and for quantifying intra-tumor heterogeneity. Identifying different types of nuclei, such as epithelial, neutrophils, lymphocytes, macrophages, etc., could yield information about the host immune response that could advance our understanding of the mechanisms governing treatment resistance and adaptive immunity in cancers of various organs. This talk will give an overview of state-of-the art machine learning algorithms for nuclei segmentation and classification from H&E stained tissue images while providing insights into the process of creating one of the largest nuclei segmentation datasets and organizing two international competitions on this theme. I will also discuss a few nuclei segmentation use-cases including automatic staging of colorectal tumors, prostate cancer recurrence prediction and, intratumor heterogeneity quantification and HER2 amplification detection in breast cancers.