Thesis
Deep Learning Approach to Histology in Gigapixel Tissue Images
Washington State University
Master of Science (MS), Washington State University
2023
DOI:
https://doi.org/10.7273/000005194
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.
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Details
- Title
- Deep Learning Approach to Histology in Gigapixel Tissue Images
- Creators
- Colin Thomas Greeley
- Contributors
- Lawrence Holder (Advisor)Yan Yan (Advisor)Michael Skinner (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Number of pages
- 78
- Identifiers
- 99901019638301842
- Language
- English
- Resource Type
- Thesis