4 citations · 15 across the 18 of their papers we have counts for
18 papers
AdaFuse: Adaptive Multimodal Fusion for Lung Cancer Risk Prediction via Reinforcement Learning
Chongyu Qu, Zhengyi Lu, Yuxiang Lai +10
Multimodal fusion has emerged as a promising paradigm for disease diagnosis and prognosis, integrating complementary information from heterogeneous data sources such as medical ima…
Self-supervised learning of imaging and clinical signatures using a multimodal joint-embedding predictive architecture
Thomas Z. Li, Aravind R. Krishnan, Lianrui Zuo +5
The development of multimodal models for pulmonary nodule diagnosis is limited by the scarcity of labeled data and the tendency for these models to overfit on the training distribu…
Cohort-Aware Agents for Individualized Lung Cancer Risk Prediction Using a Retrieval-Augmented Model Selection Framework
Chongyu Qu, Allen J. Luna, Thomas Z. Li +6
Accurate lung cancer risk prediction remains challenging due to substantial variability across patient populations and clinical settings -- no single model performs best for all co…
Multipath cycleGAN for harmonization of paired and unpaired low-dose lung computed tomography reconstruction kernels
Aravind R. Krishnan, Thomas Z. Li, Lucas W. Remedios +12
Reconstruction kernels in computed tomography (CT) affect spatial resolution and noise characteristics, introducing systematic variability in quantitative imaging measurements such…
Beyond the Lungs: Extending the Field of View in Chest CT with Latent Diffusion Models
Lianrui Zuo, Kaiwen Xu, Dingjie Su +8
The interconnection between the human lungs and other organs, such as the liver and kidneys, is crucial for understanding the underlying risks and effects of lung diseases and impr…
Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology
Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios +16
Understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions. H&E is widely available, however, cell s…
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…
Unsupervised Discovery of Clinical Disease Signatures Using Probabilistic Independence
Thomas A. Lasko, John M. Still, Thomas Z. Li +5
Insufficiently precise diagnosis of clinical disease is likely responsible for many treatment failures, even for common conditions and treatments. With a large enough dataset, it m…
Nucleus subtype classification using inter-modality learning
Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios +14
Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available b…
Inter-vendor harmonization of Computed Tomography (CT) reconstruction kernels using unpaired image translation
Aravind R. Krishnan, Kaiwen Xu, Thomas Li +9
The reconstruction kernel in computed tomography (CT) generation determines the texture of the image. Consistency in reconstruction kernels is important as the underlying CT textur…
Zero-shot CT Field-of-view Completion with Unconditional Generative Diffusion Prior
Kaiwen Xu, Aravind R. Krishnan, Thomas Z. Li +4
Anatomically consistent field-of-view (FOV) completion to recover truncated body sections has important applications in quantitative analyses of computed tomography (CT) with limit…
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…
Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation
Ho Hin Lee, Quan Liu, Shunxing Bao +7
With the inspiration of vision transformers, the concept of depth-wise convolution revisits to provide a large Effective Receptive Field (ERF) using Large Kernel (LK) sizes for med…
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…
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…