activity
20222026
most citedUNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation

4 citations · 15 across the 18 of their papers we have counts for

collaborators
Showing eess.IVShow all

10 papers · 1 filter

eess.IV2025

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…

eess.IV2024★ 1 cited

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…

eess.IV2023

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…

eess.IV2023

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…

eess.IV2023

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…

eess.IV2023★ 2 cited

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…

eess.IV2022★ 1 cited

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…

eess.IV2022★ 4 cited

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…

eess.IV2022★ 1 cited

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…

eess.IV2022★ 4 cited

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…