collaborators

11 papers

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

CT-IDP: Segmentation-Derived Quantitative Phenotypes for Interpretable Abdominal CT Disease Classification

Lavsen Dahal, Joseph Y. Lo

In this retrospective multi-institutional study, a quantitative phenotyping framework, CT-IDP (CT Image-Derived Phenotypes) was developed on the MERLIN abdominal CT benchmark (trai…

cs.CV2026

AbdomenGen: Sequential Volume-Conditioned Diffusion Framework for Abdominal Anatomy Generation

Yubraj Bhandari, Lavsen Dahal, Paul Segars +1

Computational phantoms are widely used in medical imaging research, yet current systems to generate controlled, clinically meaningful anatomical variations remain limited. We prese…

cs.CV2026

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…

eess.IV2026

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…