7 citations · 11 across the 2 of their papers we have counts for
4 papers
Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features
Ashkan Khakzar, Yang Zhang, Wejdene Mansour +5
Neural networks have demonstrated remarkable performance in classification and regression tasks on chest X-rays. In order to establish trust in the clinical routine, the networks'…
Improving Prognostic Performance in Resectable Pancreatic Ductal Adenocarcinoma using Radiomics and Deep Learning Features Fusion in CT Images
Yucheng Zhang, Edrise M. Lobo-Mueller, Paul Karanicolas +3
As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past a few years. Recent studies in radiomics aim to investigat…
CNN-based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging
Yucheng Zhang, Edrise M. Lobo-Mueller, Paul Karanicolas +3
Cox proportional hazard model (CPH) is commonly used in clinical research for survival analysis. In quantitative medical imaging (radiomics) studies, CPH plays an important role in…
Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma
Yucheng Zhang, Edrise M. Lobo-Mueller, Paul Karanicolas +3
Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer i…