Search Results for author: Zhoubing Xu

Found 18 papers, 6 papers with code

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

1 code implementation28 Sep 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

Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis.

Brain Segmentation Image Segmentation +2

Characterizing Renal Structures with 3D Block Aggregate Transformers

no code implementations4 Mar 2022 Xin Yu, Yucheng Tang, Yinchi Zhou, Riqiang Gao, Qi Yang, Ho Hin Lee, Thomas Li, Shunxing Bao, Yuankai Huo, Zhoubing Xu, Thomas A. Lasko, Richard G. Abramson, Bennett A. Landman

Efficiently quantifying renal structures can provide distinct spatial context and facilitate biomarker discovery for kidney morphology.

You May Need both Good-GAN and Bad-GAN for Anomaly Detection

no code implementations29 Sep 2021 Riqiang Gao, Zhoubing Xu, Guillaume Chabin, Awais Mansoor, Florin-Cristian Ghesu, Bogdan Georgescu, Bennett A. Landman, Sasa Grbic

A Bad-GAN generates pseudo anomalies at the low-density area of inlier distribution, and thus the inlier/outlier distinction can be approximated.

Anatomy Anomaly Detection

Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images

no code implementations18 Mar 2020 Donghao Zhang, Si-Qi Liu, Shikha Chaganti, Eli Gibson, Zhoubing Xu, Sasa Grbic, Weidong Cai, Dorin Comaniciu

In this paper, we propose a framework for liver vessel morphology reconstruction using both a fully convolutional neural network and a graph attention network.

3D Reconstruction Decision Making +2

3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles

2 code implementations28 Mar 2019 Yuankai Huo, Zhoubing Xu, Yunxi Xiong, Katherine Aboud, Prasanna Parvathaneni, Shunxing Bao, Camilo Bermudez, Susan M. Resnick, Laurie E. Cutting, Bennett A. Landman

To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location.

Brain Segmentation

Class-Aware Adversarial Lung Nodule Synthesis in CT Images

no code implementations28 Dec 2018 Jie Yang, Si-Qi Liu, Sasa Grbic, Arnaud Arindra Adiyoso Setio, Zhoubing Xu, Eli Gibson, Guillaume Chabin, Bogdan Georgescu, Andrew F. Laine, Dorin Comaniciu

Synthesizing the objects of interests, such as lung nodules, in medical images based on the distribution of annotated datasets can be helpful for improving the supervised learning tasks, especially when the datasets are limited by size and class balance.

General Classification

Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks

no code implementations9 Nov 2018 Yuankai Huo, Zhoubing Xu, Shunxing Bao, Camilo Bermudez, Hyeonsoo Moon, Prasanna Parvathaneni, Tamara K. Moyo, Michael R. Savona, Albert Assad, Richard G. Abramson, Bennett A. Landman

A clinically acquired cohort containing both T1-weighted (T1w) and T2-weighted (T2w) MRI splenomegaly scans was used to train and evaluate the performance of multi-atlas segmentation (MAS), 2D DCNN networks, and a 3D DCNN network.

Splenomegaly Segmentation On Multi-Modal Mri

SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth

1 code implementation15 Oct 2018 Yuankai Huo, Zhoubing Xu, Hyeonsoo Moon, Shunxing Bao, Albert Assad, Tamara K. Moyo, Michael R. Savona, Richard G. Abramson, Bennett A. Landman

SynSeg-Net is trained by using (1) unpaired intensity images from source and target modalities, and (2) manual labels only from source modality.

Image Segmentation Semantic Segmentation

Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images

no code implementations25 May 2018 Zhoubing Xu, Yuankai Huo, Jin-Hyeong Park, Bennett Landman, Andy Milkowski, Sasa Grbic, Shaohua Zhou

However, this is a challenging problem given not only the inherent difficulties from the ultrasound modality, e. g., low contrast and large variations, but also the heterogeneity across tasks, i. e., one classification task for all views, and then one landmark detection task for each relevant view.

Classification General Classification +1

Select, Attend, and Transfer: Light, Learnable Skip Connections

no code implementations14 Apr 2018 Saeid Asgari Taghanaki, Aicha Bentaieb, Anmol Sharma, S. Kevin Zhou, Yefeng Zheng, Bogdan Georgescu, Puneet Sharma, Sasa Grbic, Zhoubing Xu, Dorin Comaniciu, Ghassan Hamarneh

Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients.

Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth

1 code implementation20 Dec 2017 Yuankai Huo, Zhoubing Xu, Shunxing Bao, Albert Assad, Richard G. Abramson, Bennett A. Landman

Herein, we proposed a novel end-to-end synthesis and segmentation network (EssNet) to achieve the unpaired MRI to CT image synthesis and CT splenomegaly segmentation simultaneously without using manual labels on CT.

Image-to-Image Translation Medical Image Segmentation +1

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