10 citations · 23 across the 22 of their papers we have counts for
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Refining Focus in AI for Lung Cancer: Comparing Lesion-Centric and Chest-Region Models with Performance Insights from Internal and External Validation
Fakrul Islam Tushar
Background: AI-based classification models are essential for improving lung cancer diagnosis. However, the relative performance of lesion-level versus chest-region models in intern…
Peritumoral Expansion Radiomics for Improved Lung Cancer Classification
Fakrul Islam Tushar
Purpose: This study investigated how nodule segmentation and surrounding peritumoral regions influence radionics-based lung cancer classification. Methods: Using 3D CT scans with b…
XCAT-3.0: A Comprehensive Library of Personalized Digital Twins Derived from CT Scans
Lavsen Dahal, Mobina Ghojoghnejad, Dhrubajyoti Ghosh +10
Virtual Imaging Trials (VIT) offer a cost-effective and scalable approach for evaluating medical imaging technologies. Computational phantoms, which mimic real patient anatomy and…
Reproducible Benchmarking for Lung Nodule Detection and Malignancy Classification Across Multiple Low-Dose CT Datasets
Fakrul Islam Tushar, Avivah Wang, Lavsen Dahal +7
Evaluation of artificial intelligence (AI) models for low-dose CT lung cancer screening is limited by heterogeneous datasets, annotation standards, and evaluation protocols, making…
Virtual Lung Screening Trial (VLST): An In Silico Study Inspired by the National Lung Screening Trial for Lung Cancer Detection
Fakrul Islam Tushar, Liesbeth Vancoillie, Cindy McCabe +16
Clinical imaging trials play a crucial role in advancing medical innovation but are often costly, inefficient, and ethically constrained. Virtual Imaging Trials (VITs) present a so…
What limits performance of weakly supervised deep learning for chest CT classification?
Fakrul Islam Tushar, Vincent M. D'Anniballe, Geoffrey D. Rubin +1
Weakly supervised learning with noisy data has drawn attention in the medical imaging community due to the sparsity of high-quality disease labels. However, little is known about t…