Picture for Thomas Li

Thomas Li

Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation

Mar 10, 2023
Ho Hin Lee, Quan Liu, Shunxing Bao, Qi Yang, Xin Yu, Leon Y. Cai, Thomas Li, Yuankai Huo, Xenofon Koutsoukos, Bennett A. Landman

Figure 1 for Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation

Figure 2 for Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation

Figure 3 for Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation

Figure 4 for Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation

* Both codes and pretrained models are available at: https://github.com/MASILab/RepUX-Net

Access Paper or Ask Questions

UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation

Sep 28, 2022
Xin Yu, Qi Yang, Yinchi Zhou, Leon Y. Cai, Riqiang Gao, Ho Hin Lee, Thomas Li, Shunxing Bao, Zhoubing Xu, Thomas A. Lasko, Richard G. Abramson, Zizhao Zhang, Yuankai Huo, Bennett A. Landman, Yucheng Tang

Figure 1 for UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation

Figure 2 for UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation

Figure 3 for UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation

Figure 4 for UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation

* 19 pages, 17 figures. arXiv admin note: text overlap with arXiv:2203.02430

Access Paper or Ask Questions

SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains

Dec 15, 2020
Yuanqi Chen, Ge Li, Cece Jin, Shan Liu, Thomas Li

Figure 1 for SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains

Figure 2 for SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains

Figure 3 for SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains

Figure 4 for SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains

* Accepted to AAAI 2021. Code: https://github.com/cyq373/SSD-GAN

Access Paper or Ask Questions

Multi-mapping Image-to-Image Translation via Learning Disentanglement

Sep 17, 2019
Xiaoming Yu, Yuanqi Chen, Thomas Li, Shan Liu, Ge Li

Figure 1 for Multi-mapping Image-to-Image Translation via Learning Disentanglement

Figure 2 for Multi-mapping Image-to-Image Translation via Learning Disentanglement

Figure 3 for Multi-mapping Image-to-Image Translation via Learning Disentanglement

Figure 4 for Multi-mapping Image-to-Image Translation via Learning Disentanglement

* Accepted by NeurIPS 2019. Code will be available at https://github.com/Xiaoming-Yu/DMIT

Access Paper or Ask Questions