Toronto Metropolitan University
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Automatic Diagnosis of Endometrial Cancer With Deep Learning

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posted on 2024-06-17, 22:37 authored by Daniel Sherman

Diagnosing endometrial cancer early is critical to maximizing the survival rate. Currently, the best diagnostic method is manual inspection under microscope by histologists. This method can be slow, which can negatively affect survival rates. Automating diagnosis has great potential to drastically speed up the diagnostic pipeline, but has its unique challenges. Automating endometrial cancer diagnosis from biopsy slides is challenging due to the sparse availability of images as well as the gigapixel resolution of the pathology images. This thesis presents a method to automatically compress and classify endometrial biopsy slides into benign, neoplastic, and hyperplastic classes.

History

Language

eng

Degree

  • Master of Applied Science

Program

  • Biomedical Engineering

Granting Institution

Ryerson University

LAC Thesis Type

  • Thesis

Thesis Advisor

Dafna Sussman

Year

2022

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    Biomedical Engineering (Theses)

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