Search Results for author: Yu-Xing Tang

Found 12 papers, 3 papers with code

E$^2$Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans

no code implementations19 Jul 2020 Youbao Tang, Yu-Xing Tang, Yingying Zhu, Jing Xiao, Ronald M. Summers

We introduce an edge prediction module in E$^2$Net and design an edge distance map between liver and tumor boundaries, which is used as an extra supervision signal to train the edge enhanced network.

Liver Segmentation Tumor Segmentation

Cross-Domain Medical Image Translation by Shared Latent Gaussian Mixture Model

no code implementations14 Jul 2020 Yingying Zhu, You-Bao Tang, Yu-Xing Tang, Daniel C. Elton, Sung-Won Lee, Perry J. Pickhardt, Ronald M. Summers

We expect the utility of our framework will extend to other problems beyond segmentation due to the improved quality of the generated images and enhanced ability to preserve small structures.

Image-to-Image Translation Pancreas Segmentation

COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature

1 code implementation11 Jun 2020 Yifan Peng, Yu-Xing Tang, Sung-Won Lee, Yingying Zhu, Ronald M. Summers, Zhiyong Lu

(1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We trained an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR.

Anomaly Detection Computed Tomography (CT) +1

Bone Suppression on Chest Radiographs With Adversarial Learning

no code implementations8 Feb 2020 Jia Liang, Yu-Xing Tang, You-Bao Tang, Jing Xiao, Ronald M. Summers

Dual-energy (DE) chest radiography provides the capability of selectively imaging two clinically relevant materials, namely soft tissues, and osseous structures, to better characterize a wide variety of thoracic pathology and potentially improve diagnosis in posteroanterior (PA) chest radiographs.

Image-to-Image Translation SSIM

XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation

2 code implementations19 Apr 2019 Youbao Tang, Yu-Xing Tang, Jing Xiao, Ronald M. Summers

To reduce the manual annotation burden and to train a robust lung segmentor that can be adapted to pathological lungs with hazy lung boundaries, an image-to-image translation module is employed to synthesize radiorealistic abnormal CXRs from the source of normal ones for data augmentation.

Data Augmentation Image-to-Image Translation

Abnormal Chest X-ray Identification With Generative Adversarial One-Class Classifier

no code implementations5 Mar 2019 Yu-Xing Tang, You-Bao Tang, Mei Han, Jing Xiao, Ronald M. Summers

Given a chest X-ray image in the testing phase, if it is normal, the learned architecture can well model and reconstruct the content; if it is abnormal, since the content is unseen in the training phase, the model would perform poorly in its reconstruction.

One-class classifier

ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining

1 code implementation18 Jan 2019 Youbao Tang, Ke Yan, Yu-Xing Tang, Jiamin Liu, Jing Xiao, Ronald M. Summers

To address this problem, this work constructs a pseudo mask for each lesion region that can be considered as a surrogate of the real mask, based on which the Mask R-CNN is employed for lesion detection.

Computed Tomography (CT)

Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs

no code implementations19 Jul 2018 Yu-Xing Tang, Xiaosong Wang, Adam P. Harrison, Le Lu, Jing Xiao, Ronald M. Summers

In addition, highly confident samples (measured by classification probabilities) and their corresponding class-conditional heatmaps (generated by the CNN) are extracted and further fed into the AGCL framework to guide the learning of more distinctive convolutional features in the next iteration.

Curriculum Learning General Classification +1

Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection

no code implementations9 Jan 2018 Yu-Xing Tang, Josiah Wang, Xiaofang Wang, Boyang Gao, Emmanuel Dellandrea, Robert Gaizauskas, Liming Chen

This is done by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations.

Object Detection Semi-Supervised Object Detection +1

Close Yet Distinctive Domain Adaptation

no code implementations13 Apr 2017 Lingkun Luo, Xiaofang Wang, Shiqiang Hu, Chao Wang, Yu-Xing Tang, Liming Chen

Most previous research tackle this problem in seeking a shared feature representation between source and target domains while reducing the mismatch of their data distributions.

Domain Adaptation Image Classification +1

Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer

no code implementations CVPR 2016 Yu-Xing Tang, Josiah Wang, Boyang Gao, Emmanuel Dellandrea, Robert Gaizauskas, Liming Chen

This is done by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations.

Object Detection Semi-Supervised Object Detection +1

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