Joseph Y. Lo

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

2020 · 409 Zit. · JAMA Network Open

Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning- Model weights

2020 · 21 Zit. · arXiv (Cornell University)

XCAT 3.0: A comprehensive library of personalized digital twins derived from CT scans

2025 · 8 Zit. · Medical Image Analysis

Virtual imaging trials in medicine: A brief takeaway of the lessons from the first international summit

2024 · 7 Zit. · Medical Physics

Interpretable deep learning models for better clinician-AI communication in clinical mammography

2022 · 7 Zit.

Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning

2022 · 7 Zit. · BMC Medical Informatics and Decision Making

Multi-Disease Classification of 13,667 Body CT Scans Using Weakly Supervised Deep Learning

2020 · 1 Zit. · arXiv (Cornell University)

Utility of the virtual imaging trials methodology for objective characterization of AI systems and training data

2026 · 1 Zit. · Journal of Medical Imaging

Organ-Aware Attention Improves CT Triage and Classification

2026 · 0 Zit. · ArXiv.org

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

2026 · 0 Zit. · arXiv (Cornell University)

A user interface to communicate interpretable AI decisions to radiologists (Erratum)

2023 · 0 Zit.

Evaluating Large Language Models for Zero-Shot Disease Labeling in CT Radiology Reports Across Organ Systems

2025 · 0 Zit. · ArXiv.org

Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification

2026 · 0 Zit. · IEEE Access

Virtual versus reality: external validation of COVID-19 classifiers using XCAT phantoms for chest radiography

2022 · 0 Zit. · Medical Imaging 2022: Physics of Medical Imaging

Organ-Aware Attention Improves CT Triage and Classification

2026 · 0 Zit. · arXiv (Cornell University)