Doctoral Dissertations
Keywords and Phrases
Computer Vision; Neural Network; Pattern Recognition
Abstract
"The computer-aided analysis in the medical imaging field has attracted a lot of attention for the past decade. The goal of computer-vision based medical image analysis is to provide automated tools to relieve the burden of human experts such as radiologists and physicians. More specifically, these computer-aided methods are to help identify, classify and quantify patterns in medical images. Recent advances in machine learning, more specifically, in the way of deep learning, have made a big leap to boost the performance of various medical applications. The fundamental core of these advances is exploiting hierarchical feature representations by various deep learning models, instead of handcrafted features based on domain-specific knowledge.
In the work presented in this dissertation, we are particularly interested in exploring the power of deep neural network in the Circulating Tumor Cells detection and mitosis event detection. We will introduce the Convolutional Neural Networks and the designed training methodology for Circulating Tumor Cells detection, a Hierarchical Convolutional Neural Networks model and a Two-Stream Bidirectional Long Short-Term Memory model for mitosis event detection and its stage localization in phase-contrast microscopy images”--Abstract, page iii.
Advisor(s)
Yin, Zhaozheng
Committee Member(s)
Jiang, Wei
Lin, Dan
Fu, Yanjie
Qin, Ruwen
Department(s)
Computer Science
Degree Name
Ph. D. in Computer Science
Research Center/Lab(s)
Intelligent Systems Center
Publisher
Missouri University of Science and Technology
Publication Date
Spring 2018
Pagination
ix, 54 pages
Note about bibliography
Includes bibliographic references (pages 46-53).
Rights
© 2018 Yunxiang Mao, All rights reserved.
Document Type
Dissertation - Open Access
File Type
text
Language
English
Thesis Number
T 12090
Recommended Citation
Mao, Yunxiang, "Detecting cells and analyzing their behaviors in microscopy images using deep neural networks" (2018). Doctoral Dissertations. 3135.
https://scholarsmine.mst.edu/doctoral_dissertations/3135
Comments
The author would like to extend my thanks to the Department of Computer Science and the Intelligent System Center (ISC) at Missouri S & T, and the National Science Foundation (NSF) for supporting my PhD program.