Doctoral Dissertations


Yunxiang Mao

Keywords and Phrases

Computer Vision; Neural Network; Pattern Recognition


"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.


Yin, Zhaozheng

Committee Member(s)

Jiang, Wei
Lin, Dan
Fu, Yanjie
Qin, Ruwen


Computer Science

Degree Name

Ph. D. in Computer Science


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.

Research Center/Lab(s)

Intelligent Systems Center


Missouri University of Science and Technology

Publication Date

Spring 2018


ix, 54 pages

Note about bibliography

Includes bibliographic references (pages 46-53).


© 2018 Yunxiang Mao, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Thesis Number

T 12090