9 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…
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…
ORACLE-CT: Anatomy-Aware Support Pooling for CT Classification
Lavsen Dahal, Yubraj Bhandari, Geoffrey Rubin +1
Abdominal CT disease classification is challenging because each scan is a large 3D volume with many possible findings, while diagnostic evidence is often confined to specific organ…
JANUS: Anatomy-Conditioned Gating for Robust CT Triage Under Distribution Shift
Lavsen Dahal, Yubraj Bhandari, Geoffrey Rubin +1
Automated CT triage requires models that are simultaneously accurate across diverse pathologies and reliable under institutional shift. While Vision Transformers provide strong vis…
STAMP: Selective Task-Aware Mechanism for Text Privacy
Fengwei Tian, Payel Bhattacharjee, Heidi Hanson +3
We present STAMP (Selective Task-Aware Mechanism for Text Privacy), a new framework for task-aware text privatization that achieves an improved privacy-utility trade-off. STAMP sel…
Organ-Aware Attention Improves CT Triage and Classification
Lavsen Dahal, Yubraj Bhandari, Geoffrey D. Rubin +1
There is an urgent need for triage and classification of high-volume medical imaging modalities such as computed tomography (CT), which can improve patient care and mitigate radiol…
Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification
Payel Bhattacharjee, Fengwei Tian, Geoffrey D. Rubin +5
Large Language Models (LLMs) are increasingly adopted across domains such as education, healthcare, and finance. In healthcare, LLMs support tasks including disease diagnosis, abno…
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…
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…