10 citations · 33 across the 25 of their papers we have counts for
26 papers
Distilling CT Foundation Models into Editable Concept Bottlenecks for Lung Nodule Malignancy Prediction
Fakrul Islam Tushar, Stephen Adamo, Geoffrey D. Rubin
Foundation models provide transferable CT representations, but predictions based directly on these embeddings are difficult to interpret. We developed concept bottleneck models tha…
Synthetic CT as a First Safeguard for Choosing Medical VLMs in Lung Cancer Screening
Fakrul Islam Tushar, Geoffrey D. Rubin
Many medical vision--language models (VLMs) are publicly available, but a site adopting one for a new imaging task rarely has the labeled real data needed to compare them. Because…
iTRIALSPACE: Programmable Virtual Lesion Trials for Controlled Evaluation of Lung CT Models
Fakrul Islam Tushar, Umme Hafsa Momy, Joseph Y. Lo +1
We introduce iTRIALSPACE, a programmable evaluation framework for controlled assessment of lung CT models. Standard benchmarks are static retrospective collections that entangle le…
Tri-Reader: An Open-Access, Multi-Stage AI Pipeline for First-Pass Lung Nodule Annotation in Screening CT
Fakrul Islam Tushar, Joseph Y. Lo
Using multiple open-access models trained on public datasets, we developed Tri-Reader, a comprehensive, freely available pipeline that integrates lung segmentation, nodule detectio…
NodMAISI: Nodule-Oriented Medical AI for Synthetic Imaging
Fakrul Islam Tushar, Ehsan Samei, Cynthia Rudin +1
Objective: Although medical imaging datasets are increasingly available, abnormal and annotation-intensive findings critical to lung cancer screening, particularly small pulmonary…
Demographic Distribution Matching between real world and virtual phantom population
Dhrubajyoti Ghosh, Fakrul Islam Tushar, Lavsen Dahal +5
Virtual imaging trials (VITs) offer scalable and cost-effective tools for evaluating imaging systems and protocols. However, their translational impact depends on rigorous comparab…
Zero-Shot Multi-Disease Labeling of Chest, Abdomen, and Pelvis CT Reports Using Open-Weight Large Language Models: The Effect of Labeling Conventions
Michael E. Garcia-Alcoser, Mobina Ghojoghnejad, Fakrul Islam Tushar +4
Purpose: To compare five lightweight open-weight large language models (LLMs) with a rule-based algorithm (RBA) and fine-tuned RadBERT for zero-shot labeling of chest-abdomen-pelvi…
Virtual Patients, Real Gains: Digital Twin-Based Simulated CT for Multitask Lung Nodule Analysis
Fakrul Islam Tushar, Lavsen Dahal, Paul Segars +1
AI-based lung cancer screening is constrained by scarce, annotated CT data, particularly for rare nodule presentations. We investigate whether physics-based, anatomy-informed simul…
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…
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…
Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning
Vincent M. D'Anniballe, Fakrul Islam Tushar, Khrystyna Faryna +4
Purpose: To develop high throughput multi-label annotators for body (chest, abdomen, and pelvis) Computed Tomography (CT) reports that can be applied across a variety of abnormalit…
Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features
Anindo Saha, Fakrul I. Tushar, Khrystyna Faryna +5
Weakly supervised disease classification of CT imaging suffers from poor localization owing to case-level annotations, where even a positive scan can hold hundreds to thousands of…
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…
DSNet: Automatic Dermoscopic Skin Lesion Segmentation
Md. Kamrul Hasan, Lavsen Dahal, Prasad N. Samarakoon +2
Automatic segmentation of skin lesion is considered a crucial step in Computer Aided Diagnosis (CAD) for melanoma diagnosis. Despite its significance, skin lesion segmentation rema…
Comparative Analysis of Automatic Skin Lesion Segmentation with Two Different Implementations
Md. Kamrul Hasan, Basel Alyafi, Fakrul Islam Tushar
Lesion segmentation from the surrounding skin is the first task for developing automatic Computer-Aided Diagnosis of skin cancer. Variant features of lesion like uneven distributio…
Brain Tissue Segmentation Using NeuroNet With Different Pre-processing Techniques
Fakrul Islam Tushar, Basel Alyafi, Md. Kamrul Hasan +1
Automatic segmentation of brain Magnetic Resonance Imaging (MRI) images is one of the vital steps for quantitative analysis of brain for further inspection. In this paper, NeuroNet…