4 citations · 20 across the 15 of their papers we have counts for
15 papers
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…
Tractography with T1-weighted MRI and associated anatomical constraints on clinical quality diffusion MRI
Tian Yu, Yunhe Li, Michael E. Kim +8
Diffusion MRI (dMRI) streamline tractography, the gold standard for in vivo estimation of brain white matter (WM) pathways, has long been considered indicative of macroscopic relat…
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…
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…
Robust Fiber Orientation Distribution Function Estimation Using Deep Constrained Spherical Deconvolution for Diffusion MRI
Tianyuan Yao, Francois Rheault, Leon Y Cai +11
Diffusion-weighted magnetic resonance imaging (DW-MRI) is a critical imaging method for capturing and modeling tissue microarchitecture at a millimeter scale. A common practice to…
Exploring shared memory architectures for end-to-end gigapixel deep learning
Lucas W. Remedios, Leon Y. Cai, Samuel W. Remedios +8
Deep learning has made great strides in medical imaging, enabled by hardware advances in GPUs. One major constraint for the development of new models has been the saturation of GPU…
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…
A Unified Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-weighted MRI
Tianyuan Yao, Nancy Newlin, Praitayini Kanakaraj +6
Diffusion-weighted (DW) MRI measures the direction and scale of the local diffusion process in every voxel through its spectrum in q-space, typically acquired in one or more shells…
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…
Single Slice Thigh CT Muscle Group Segmentation with Domain Adaptation and Self-Training
Qi Yang, Xin Yu, Ho Hin Lee +8
Objective: Thigh muscle group segmentation is important for assessment of muscle anatomy, metabolic disease and aging. Many efforts have been put into quantifying muscle tissues wi…
Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation
Ho Hin Lee, Yucheng Tang, Han Liu +7
Recent studies have demonstrated the superior performance of introducing ``scan-wise" contrast labels into contrastive learning for multi-organ segmentation on multi-phase computed…
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…
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…
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation
Ho Hin Lee, Yucheng Tang, Qi Yang +6
Medical image segmentation, or computing voxelwise semantic masks, is a fundamental yet challenging task to compute a voxel-level semantic mask. To increase the ability of encoder-…
Joint analysis of structural connectivity and cortical surface features: correlates with mild traumatic brain injury
Cailey I. Kerley, Leon Y. Cai, Chang Yu +7
Mild traumatic brain injury (mTBI) is a complex syndrome that affects up to 600 per 100,000 individuals, with a particular concentration among military personnel. About half of all…