EECS – Thesis Defense: Deep Learning Approach to Histology in Gigapixel Image, Colin Greeley

About the event

Student: Colin Greeley

Advisor: Dr. Lawrence Holder

Degree: Computer Science MS

Thesis Title: Deep Learning Approach to Histology in Gigapixel Image

Abstract: 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.