Year: 2019
Type: dissertation
Abstract: Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone, is limited due to high correlation among features and the multiple testing problem. Deep learning architectures, such as Convolutional Neural networks (CNNs) have been shown to outperform traditional feature-based approaches in computer vision tasks such as object detection. Nonetheless, they require large sample sizes for training which limits their application in medical imaging. As an alternative solution, CNN-based transfer learning has shown potential for achieving reasonable performance using datasets with small sample sizes. In this work, we developed a CNN-based deep radiomics pipeline based on transfer learning, which outperforms the traditional radiomics model in resectable PDAC prognostication.
Source: TSpace (University of Toronto)
Author: Yucheng Zhang
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2995941984