000008668 001__ 8668
000008668 005__ 20251007005915.0
000008668 0247_ $$2DOI$$a10.6083/ks65hc876
000008668 037__ $$aETD
000008668 245__ $$aArtificially intelligent pathology
000008668 260__ $$bOregon Health and Science University
000008668 269__ $$a2020
000008668 336__ $$aDissertation
000008668 502__ $$bPh.D.
000008668 502__ $$gBiomedical Engineering
000008668 520__ $$aControlling cancer requires comprehensive understanding of the molecular, cellular, and organizational properties of tumor tissue. While clinical pathology has served as a gold standard for cancer diagnosis for over a century, the field continues to largely rely on visual inspection of sectioned and stained tissue under the microscope by expert pathologists. This work integrates deep learning systems for histopathological image analysis to quantitatively and qualitatively evaluate spatial characteristics of tumor biology to better guide clinical diagnosis and treatment of cancer.
000008668 542__ $$fIn copyright - single owner
000008668 650__ $$aArtificial Intelligence$$015109
000008668 650__ $$aData Management$$013037
000008668 650__ $$aPathology$$023652
000008668 650__ $$aImage Processing, Computer-Assisted$$020650
000008668 691__ $$aSchool of Medicine$$041369
000008668 692__ $$aDepartment of Biomedical Engineering$$041397
000008668 7001_ $$aSchau, Geoffrey$$uOregon Health and Science University$$041354
000008668 7201_ $$aChang, Young Hwan$$uOregon Health and Science University$$041354$$7Personal$$eAdvisor
000008668 8564_ $$980d37601-976a-45d8-b9f0-bd5c23e4b4b8$$s72952201$$uhttps://digitalcollections.ohsu.edu/record/8668/files/Schau.Geoffrey.2020.pdf$$eEmbargo (2023-10-27)$$237bba6beaf499ca151205295afb25da1$$31
000008668 905__ $$a/rest/prod/ks/65/hc/87/ks65hc876
000008668 909CO $$ooai:digitalcollections.ohsu.edu:8668$$pstudent-work
000008668 980__ $$aTheses and Dissertations