7 citations · 9 across the 13 of their papers we have counts for
21 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…
When Does Synthetic CT Transfer? A Label-Free Donor/Host Diagnostic for Medical Vision-Language Model Routing on Real Lung CT
Fakrul Islam Tushar
A synthetic measurement of model competence is useful only if it survives the move to real data, yet the real labels that would verify it are exactly what medical imaging lacks. We…
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…