Investigation of KimiaNet's and DenseNet's deep features in lung cancer subtypes

Deep neural networks (DNN) have extended applications in the _eld of digital pathology. One of which is to act as feature extractors for content-based image retrieval (CBIR) systems. Therefore, it is necessary to investigate how these deep features work and attribute these features to histologic patterns. This study showed that the median of deep feature value could serve as a simple yet efficient representation of whole slide images (WSI). Through exploring deep features of lung cancer, it was discovered that some of these deep features have strong correlations with either lung adenocarcinoma (LUAD) or lung squamous carcinoma (LUSC). A deep feature-specific visualization technique was proposed for analyzing deep features at WSI-level. These prominent deep features were also generalizable to cancers of other organs, namely kidney and brain. However, deep features did not exhibit a potential for cancer grade and somatic mutation state classification.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Dehkharghanian, Taher

Advisors dc:contributor.advisor

• Rahnamayan, Shahryar
• Tizhoosh, Hamid R.

• Histopathology
• Content-Based Image Retrieval
• Deep Neural Networks
• Lung cancer
• Deep features

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1540

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1540