AI Seminar Series 2022: Ruchika Verma

The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.

On May 13, 2022, Ruchika Verma, a machine learning scientist at Amii, presented "Machine Learning to Predict Survival and Treatment Outcomes for Cancer Patients," at the AI Seminar.

Manual assessment of medical images is challenging due to high intra- and inter-observer variability. With improvements in computer vision techniques and hardware, it is now possible to quantitatively assess subtle visual features in histopathology and diagnostic images that are usually difficult to evaluate manually. Tumour and nuclei segmentation is one of the key modules in histopathological image analysis that could facilitate downstream analysis of tissue samples for assessing not only cancer grades or stages but also for predicting tumour recurrence, treatment effectiveness, and for quantifying intra-tumour 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.

Ruchika’s presentation gives 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. Shel also discusses a few projects on applying machine learning algorithms for the identification of co-existing multiple molecular subtypes of breast cancer in a patient, radiomics-based treatment outcome prediction in Glioblastoma patients, and personalized survival prediction from pan-cancer whole transcriptome data.

Watch the full presentation below:

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