Award Date
May 2024
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Committee Member
Mingon Kang
Second Committee Member
Laxmi Gewali
Third Committee Member
Fatma Nasoz
Fourth Committee Member
Shaikh Arifuzzaman
Fifth Committee Member
Mira Han
Number of Pages
30
Abstract
The acid-fast stain is frequently used for laboratory diagnosis of tuberculosis. It is a labor intensive task requiring thorough examination of extremely high-resolution images to pinpoint the presence of the mycobacteria. This paper presents a machine learning assisted slide image analysis tool with the aim of aiding histopathology professionals in the accurate diagnosis of tuberculosis in patients through the analysis of microscopic imagery. The proposed tool combines a digital whole slide image viewer with an online learning framework. We also conducted a survey of different state-of-the-art online learning methods, and found that MIR with pre-training has the best performance on the CIFAR-10 dataset.
Keywords
Acid-Fast Staining; Histopathology; Online Learning; Tuberculosis
Disciplines
Computer Sciences
Degree Grantor
University of Nevada, Las Vegas
Language
English
Repository Citation
Wang, Shizhao, "Online Learning for Acid-Fast Bacilli Detection in Histopathological Images" (2024). UNLV Theses, Dissertations, Professional Papers, and Capstones. 5093.
https://digitalscholarship.unlv.edu/thesesdissertations/5093
Rights
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