Abstract
In this dissertation, we propose novel machine learning algorithms for high-risk medical imaging applications. Specifically, we tackle current challenges in radiology screening process and introduce cutting-edge methods for image-based diagnosis, detection and segmentation. We incorporate expert knowledge through eye-tracking, making the whole process human-centered. This dissertation contributes to machine learning, computer vision, and medical imaging research by: 1) introducing a mathematical formulation of radiologists level of attention, and sparsifying their gaze data for a better extraction and comparison of search patterns. 2) proposing novel, local and global, image analysis algorithms. Imaging based diagnosis and pattern analysis are "high-risk" Artificial Intelligence applications. A standard radiology screening procedure includes detection, diagnosis and measurement (often done with segmentation) of abnormalities. We hypothesize that having a true collaboration is essential for a better control mechanism, in such applications. In this regard, we propose to form a collaboration medium between radiologists and machine learning algorithms through eye-tracking. Further, we build a generic platform consisting of novel machine learning algorithms for each of these tasks. Our collaborative algorithm utilizes eye tracking and includes an attention model and gaze-pattern analysis, based on data clustering and graph sparsification. Then, we present a semi-supervised multi-task network for local analysis of image in radiologists' ROIs, extracted in the previous step. To address missing tumors and analyze regions that are completely missed by radiologists during screening, we introduce a detection framework, S4ND: Single Shot Single Scale Lung Nodule Detection. Our proposed detection algorithm is specifically designed to handle tiny abnormalities in lungs, which are easy to miss by radiologists. Finally, we introduce a novel projective adversarial framework, PAN: Projective Adversarial Network for Medical Image Segmentation, for segmenting complex 3D structures/organs, which can be beneficial in the screening process by guiding radiologists search areas through segmentation of desired structure/organ.
Notes
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Graduation Date
2019
Semester
Fall
Advisor
Bagci, Ulas
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Computer Science
Degree Program
Computer Science
Format
application/pdf
Identifier
CFE0008282; DP0023653
Language
English
Release Date
June 2020
Length of Campus-only Access
None
Access Status
Doctoral Dissertation (Open Access)
STARS Citation
Khosravan, Naji, "Collaborative Artificial Intelligence Algorithms for Medical Imaging Applications" (2019). Electronic Theses and Dissertations. 6877.
https://stars.library.ucf.edu/etd/6877