Ashwin Raju

ORCID Identifier(s)

0000-0002-4110-3757

Graduation Semester and Year

2022

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Computer Science

Department

Computer Science and Engineering

First Advisor

Junzhou Huang

Abstract

Digital Pathology (DP) has been recently used in replacement to traditional microscopy samples as it easy to navigate and can be analysed, processed and saved. With the invention of Digital pathology, there has been exponential increase of automated process to make the life of Doctors easier. One such automated process is Artificial Intelligence (AI) where the AI is used as an assistant to Humans and to make the analysis and guide the experts. With the advent of AI and in particular Deep Learning, research has been divided and focused to solve multiple problems in Digital Pathology. One such important application is to analyse the Whole Slide images (WSIs) of patients and predict Cancer stages for the patient. This is crucial because the WSIs becomes too many which requires Expert knowledge and time consuming job. This make a perfect application where Deep learning can be used. In this dissertation, we address the problem of identifying WSIs of patients and predict the cancer stages. We further identify several important observations to improve the performance of WSIs. We extract the granular details of the WSIs and capture the spatial relationship of granular features. We use these Graph of granualr features to further classify the cancer stages of patients. We conduct several experiments to prove our work experimentally and make conclusion that the proposed work can be used to predict cancer stages.

Keywords

Cancer staging, Whole slide image, Histology image, Segmentation

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

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