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DTSTART;TZID="Pacific Time (US & Canada)":20230412T130000
DTEND;TZID="Pacific Time (US & Canada)":20230412T150000
SUMMARY:EECS – Thesis Defense: Deep Learning Approach to Histology in Gigapixel Image, Colin Greeley
LOCATION:Online
DESCRIPTION:Student: Colin Greeley\n\nAdvisor: Dr. Lawrence Holder\n\nDegree: Computer Science MS\n\nThesis Title: Deep Learning Approach to Histology in Gigapixel Image\n\nAbstract: Deep learning has shown superior performance for automating image recognition tasks, exceeding human capabilities in both time and accuracy. Histopathology diagnostics is one of the more popular challenges at the intersection of artificial intelligence, computer vision, and medicine. Developing methods to automatically segment and detect pathologies in digitized histology slides imposes unique challenges due to the large size of these images and the complexity of the features present in biological tissue. Most methods that are capable of human-level recognition in histopathology are tuned to a specific problem since the computational complexity exceeds that of traditional image classification problems. In this paper, a deep learning approach is presented that can be trained to locate and accurately classify different types of pathologies in gigapixel digitized histology slides along with completing the binary disease classification for the entire image. The approach is trained and validated on a wide variety of tissue types and pathologies taken from an epigenetics study at Washington State University along with validation on public datasets.\n\n \n\n 
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