2 citations · 7 across the 7 of their papers we have counts for
7 papers
The Utility of the Virtual Imaging Trials Methodology for Objective Characterization of AI Systems and Training Data
Fakrul Islam Tushar, Lavsen Dahal, Saman Sotoudeh-Paima +4
Purpose: The credibility of Artificial Intelligence (AI) models for medical imaging continues to be a challenge, affected by the diversity of models, the data used to train the mod…
Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography
Fakrul Islam Tushar, Ehsan Abadi, Saman Sotoudeh-Paima +5
Research studies of artificial intelligence models in medical imaging have been hampered by poor generalization. This problem has been especially concerning over the last year with…
Quality or Quantity: Toward a Unified Approach for Multi-organ Segmentation in Body CT
Fakrul Islam Tushar, Husam Nujaim, Wanyi Fu +5
Organ segmentation of medical images is a key step in virtual imaging trials. However, organ segmentation datasets are limited in terms of quality (because labels cover only a few…
Co-occurring Diseases Heavily Influence the Performance of Weakly Supervised Learning Models for Classification of Chest CT
Fakrul Islam Tushar, Vincent M. D'Anniballe, Geoffrey D. Rubin +2
Despite the potential of weakly supervised learning to automatically annotate massive amounts of data, little is known about its limitations for use in computer-aided diagnosis (CA…
iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry
Wanyi Fu, Shobhit Sharma, Ehsan Abadi +6
Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins (DT) using patient medical imag…
Classification of Multiple Diseases on Body CT Scans using Weakly Supervised Deep Learning
Fakrul Islam Tushar, Vincent M. D'Anniballe, Rui Hou +5
Purpose: To design multi-disease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports.Materials & Method…