7 citations · 14 across the 9 of their papers we have counts for
9 papers
No winners: Performance of lung cancer prediction models depends on screening-detected, incidental, and biopsied pulmonary nodule use cases
Thomas Z. Li, Kaiwen Xu, Aravind Krishnan +14
Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease. Many models have been pu…
Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification
Thomas Z. Li, John M. Still, Kaiwen Xu +11
The accuracy of predictive models for solitary pulmonary nodule (SPN) diagnosis can be greatly increased by incorporating repeat imaging and medical context, such as electronic hea…
Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension Perspective
Kaiwen Xu, Thomas Li, Mirza S. Khan +6
Field-of-view (FOV) tissue truncation beyond the lungs is common in routine lung screening computed tomography (CT). This poses limitations for opportunistic CT- based body composi…
A Comparative Study of Confidence Calibration in Deep Learning: From Computer Vision to Medical Imaging
Riqiang Gao, Thomas Li, Yucheng Tang +7
Although deep learning prediction models have been successful in the discrimination of different classes, they can often suffer from poor calibration across challenging domains inc…
Deep Multi-path Network Integrating Incomplete Biomarker and Chest CT Data for Evaluating Lung Cancer Risk
Riqiang Gao, Yucheng Tang, Kaiwen Xu +7
Clinical data elements (CDEs) (e.g., age, smoking history), blood markers and chest computed tomography (CT) structural features have been regarded as effective means for assessing…
Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection
Yiyuan Yang, Riqiang Gao, Yucheng Tang +6
Recently, multi-task networks have shown to both offer additional estimation capabilities, and, perhaps more importantly, increased performance over single-task networks on a "main…