Automating computed tomography diagnostic image quality control using deep learning
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Gupta, Anubhav
issued: 2024-01-10T19:02:39Z
issued: 2023
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Medical imaging quality control is traditionally done through routine manual testing, which is time-consuming, error-prone, and costly. There has been limited exploration of automating this quality control process using deep learning techniques. Several factors contribute to this gap in research, including 1) the need for a specialized labelled dataset to evaluate this problem, 2) the scarcity of labelled medical imaging data for training state-of-the-art deep learning models capable of robust generalization on medical imaging classification tasks, and 3) the absence of a dedicated deep learning backbone pre-trained on medical imaging data capable of robust generalization for various medical image analysis tasks. This thesis first presents a study that evaluates state-of-the-art deep convolutional neural network models to automate the quality control process for medical images. By harnessing transfer learning, the goal is to enhance the availability and effectiveness of medical image diagnostic scanners for applications in patient diagnosis, treatment, research, and development. We evaluate the performance of 5 different models wherein InceptionResNetV2 achieves the best accuracy of 99%. However, when these models are tested on out-of-domain datasets, the results show a crucial need for more robust models capable of automating quality control within different healthcare facilities. In the second part of the thesis, we employ a self-supervised approach to pre-train a specialized backbone, referred to as MedMAE, using a diverse dataset comprising over two million publicly available medical images. Our results demonstrate that MedMAE effectively captures the unique characteristics of medical images, leading to improved accuracy in downstream medical imaging tasks and even on out-of-domain datasets. This innovative approach holds promise for enhancing the utility and efficiency of medical image analysis, bridging the gap caused by the scarcity of labelled data in the he…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/87199
note: Automating Computed TomographyDiagnostic Image Quality ControlUsing Deep LearningbyAnubhav GuptaB.E., BITS Pilani Dubai Campus, 2020A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe College of Graduate Studies(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)December 2023© Anubhav Gupta, 2023The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis/dissertation en-titled:Automating Computed Tomography Diagnostic Image Quality ControlUsing Deep Learningsubmitted by Anubhav Gupta in partial fulfilment of the requirements ofthe degree of Master of ScienceW. John Braun, The University of British Columbia, Okanagan CampusSupervisorMohamed S. Shehata, The University of British Columbia, Okanagan CampusCo-SupervisorPourang Irani, The University of British Columbia, Okanagan CampusSupervisory Committee MemberJohn Thompson, The University of British Columbia, Okanagan CampusSupervisory Committee MemberKlaske Van Heusden, The University of British Columbia, Okanagan CampusUniversity ExamineriiAbstractMedical imaging quality control is traditionally done through routinemanual testing, which is time-consuming, error-prone, and costly. Therehas been limited exploration of automating this quality control process us-ing deep learning techniques. Several factors contribute to this gap in re-search, including 1) the need for a specialized labelled dataset to evaluatethis problem, 2) the scarcity of labelled medical imaging data for trainingstate-of-the-art deep learning models capable of robust generalization onmedical imaging classification tasks, and 3) the absence of a dedicated deeplearning backbone pre-trained on medical imaging data capable of robustgeneralization for various medical image analysis tasks. This thesis firstpresents a study that evaluates state-of-the-art deep convolutional neuralnetwork models to automate the quality control proce…
has type: Thesis/Dissertation
date issued: 2024-02
is shown at: 10.14288/1.0438613
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Braun, John, 1963-
contributor: Shehata, Mohamed S.
type: Text
identifier URI: http://hdl.handle.net/2429/87199