7 citations · 40 across the 24 of their papers we have counts for
25 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…
Deep conditional generative models for longitudinal single-slice abdominal computed tomography harmonization
Xin Yu, Qi Yang, Yucheng Tang +8
Two-dimensional single-slice abdominal computed tomography (CT) provides a detailed tissue map with high resolution allowing quantitative characterization of relationships between…
COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training
Han Liu, Zhoubing Xu, Riqiang Gao +5
Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, med…
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…
Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models
Xin Yu, Qi Yang, Yucheng Tang +8
2D low-dose single-slice abdominal computed tomography (CT) slice enables direct measurements of body composition, which are critical to quantitatively characterizing health relati…
Longitudinal Variability Analysis on Low-dose Abdominal CT with Deep Learning-based Segmentation
Xin Yu, Yucheng Tang, Qi Yang +6
Metabolic health is increasingly implicated as a risk factor across conditions from cardiology to neurology, and efficiency assessment of body composition is critical to quantitati…
Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography
Thomas Z. Li, Kaiwen Xu, Riqiang Gao +5
Features learned from single radiologic images are unable to provide information about whether and how much a lesion may be changing over time. Time-dependent features computed fro…
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
Xin Yu, Qi Yang, Yinchi Zhou +12
Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical…
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…
Pseudo-Label Guided Multi-Contrast Generalization for Non-Contrast Organ-Aware Segmentation
Ho Hin Lee, Yucheng Tang, Riqiang Gao +7
Non-contrast computed tomography (NCCT) is commonly acquired for lung cancer screening, assessment of general abdominal pain or suspected renal stones, trauma evaluation, and many…
Characterizing Renal Structures with 3D Block Aggregate Transformers
Xin Yu, Yucheng Tang, Yinchi Zhou +10
Efficiently quantifying renal structures can provide distinct spatial context and facilitate biomarker discovery for kidney morphology. However, the development and evaluation of t…
Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging
Shunxing Bao, Yucheng Tang, Ho Hin Lee +8
Multiplex immunofluorescence (MxIF) is an emerging imaging technique that produces the high sensitivity and specificity of single-cell mapping. With a tenet of 'seeing is believing…
Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT
Riqiang Gao, Mirza S. Khan, Yucheng Tang +6
Image Quality Assessment (IQA) is important for scientific inquiry, especially in medical imaging and machine learning. Potential data quality issues can be exacerbated when human-…
Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective
Riqiang Gao, Yucheng Tang, Kaiwen Xu +7
Data from multi-modality provide complementary information in clinical prediction, but missing data in clinical cohorts limits the number of subjects in multi-modal learning contex…
Development and Characterization of a Chest CT Atlas
Kaiwen Xu, Riqiang Gao, Mirza S. Khan +8
A major goal of lung cancer screening is to identify individuals with particular phenotypes that are associated with high risk of cancer. Identifying relevant phenotypes is complic…
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…
Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives
Yuchen Xu, Olivia Tang, Yucheng Tang +9
Segmentation of abdominal computed tomography(CT) provides spatial context, morphological properties, and a framework for tissue-specific radiomics to guide quantitative Radiologic…
Outlier Guided Optimization of Abdominal Segmentation
Yuchen Xu, Olivia Tang, Yucheng Tang +9
Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive research interest. It presents a substantial challenge in medical image proc…
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…
Contrast Phase Classification with a Generative Adversarial Network
Yucheng Tang, Ho Hin Lee, Yuchen Xu +10
Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context…
Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision
Ho Hin Lee, Yucheng Tang, Olivia Tang +9
Human in-the-loop quality assurance (QA) is typically performed after medical image segmentation to ensure that the systems are performing as intended, as well as identifying and e…
Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging
Riqiang Gao, Lingfeng Li, Yucheng Tang +6
Annual low dose computed tomography (CT) lung screening is currently advised for individuals at high risk of lung cancer (e.g., heavy smokers between 55 and 80 years old). The reco…
Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection
Riqiang Gao, Yuankai Huo, Shunxing Bao +9
The field of lung nodule detection and cancer prediction has been rapidly developing with the support of large public data archives. Previous studies have largely focused on cross-…