Youbao Tang

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National Institutes of Health ∙ Harbin Institute of Technology

Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection

Measuring lesion size is an important step to assess tumor growth and mo...
05/05/2021 ∙ by Youbao Tang, et al. ∙ 0 ∙ share

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Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss

Accurately segmenting a variety of clinically significant lesions from w...
05/03/2021 ∙ by Youbao Tang, et al. ∙ 0 ∙ share

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Computational Emotion Analysis From Images: Recent Advances and Future Directions

Emotions are usually evoked in humans by images. Recently, extensive res...
03/19/2021 ∙ by Sicheng Zhao, et al. ∙ 3 ∙ share

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Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining

The boundary of tumors (hepatocellular carcinoma, or HCC) contains rich ...
03/09/2021 ∙ by Jieneng Chen, et al. ∙ 0 ∙ share

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Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies

Monitoring treatment response in longitudinal studies plays an important...
12/09/2020 ∙ by Jinzheng Cai, et al. ∙ 17 ∙ share

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Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images

Radiological images such as computed tomography (CT) and X-rays render a...
12/04/2020 ∙ by Ke Yan, et al. ∙ 0 ∙ share

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One Click Lesion RECIST Measurement and Segmentation on CT Scans

In clinical trials, one of the radiologists' routine work is to measure ...
07/21/2020 ∙ by Youbao Tang, et al. ∙ 0 ∙ share

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

Developing an effective liver and liver tumor segmentation model from CT...
07/19/2020 ∙ by Youbao Tang, et al. ∙ 0 ∙ share

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

Current deep learning based segmentation models often generalize poorly ...
07/14/2020 ∙ by Yingying Zhu, et al. ∙ 1 ∙ share

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Bone Suppression on Chest Radiographs With Adversarial Learning

Dual-energy (DE) chest radiography provides the capability of selectivel...
02/08/2020 ∙ by Jia Liang, et al. ∙ 0 ∙ share

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Weakly Supervised Lesion Co-segmentation on CT Scans

Lesion segmentation in medical imaging serves as an effective tool for a...
01/24/2020 ∙ by Vatsal Agarwal, et al. ∙ 18 ∙ share

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Weakly-Supervised Lesion Segmentation on CT Scans using Co-Segmentation

Lesion segmentation on computed tomography (CT) scans is an important st...
01/23/2020 ∙ by Vatsal Agarwal, et al. ∙ 8 ∙ share

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TUNA-Net: Task-oriented UNsupervised Adversarial Network for Disease Recognition in Cross-Domain Chest X-rays

In this work, we exploit the unsupervised domain adaptation problem for ...
08/21/2019 ∙ by Yuxing Tang, et al. ∙ 0 ∙ share

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MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation

When reading medical images such as a computed tomography (CT) scan, rad...
08/12/2019 ∙ by Ke Yan, et al. ∙ 6 ∙ share

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

This paper proposes a novel framework for lung segmentation in chest X-r...
04/19/2019 ∙ by Youbao Tang, et al. ∙ 0 ∙ share

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

Being one of the most common diagnostic imaging tests, chest radiography...
03/05/2019 ∙ by Yuxing Tang, et al. ∙ 0 ∙ share

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

Automatic lesion detection from computed tomography (CT) scans is an imp...
01/18/2019 ∙ by Youbao Tang, et al. ∙ 2 ∙ share

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CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement

Automated lesion segmentation from computed tomography (CT) is an import...
07/18/2018 ∙ by Youbao Tang, et al. ∙ 0 ∙ share

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Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST

Volumetric lesion segmentation from computed tomography (CT) images is a...
07/02/2018 ∙ by Jinzheng Cai, et al. ∙ 0 ∙ share

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Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks

Response evaluation criteria in solid tumors (RECIST) is the standard me...
06/25/2018 ∙ by Youbao Tang, et al. ∙ 6 ∙ share

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CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation

Data availability plays a critical role for the performance of deep lear...
06/11/2018 ∙ by Dakai Jin, et al. ∙ 2 ∙ share

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Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST

Volumetric lesion segmentation via medical imaging is a powerful means t...
01/25/2018 ∙ by Jinzheng Cai, et al. ∙ 0 ∙ share

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Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs

This paper proposes a novel saliency detection method by combining regio...
08/18/2016 ∙ by Youbao Tang, et al. ∙ 0 ∙ share

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Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection

This paper proposes a novel saliency detection method by developing a de...
08/18/2016 ∙ by Youbao Tang, et al. ∙ 0 ∙ share

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Youbao Tang

Featured Co-authors

Jing Xiao

Le Lu

Ya Zhang

Lin Yang

Ronald M. Summers

Alan L. Yuille

Björn W. Schuller

Ke Yan

Adam P. Harrison

Zhiyong Lu

Yifan Peng

Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection

Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss

Computational Emotion Analysis From Images: Recent Advances and Future Directions

Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining

Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies

Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images

One Click Lesion RECIST Measurement and Segmentation on CT Scans

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

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

Bone Suppression on Chest Radiographs With Adversarial Learning

Weakly Supervised Lesion Co-segmentation on CT Scans

Weakly-Supervised Lesion Segmentation on CT Scans using Co-Segmentation

TUNA-Net: Task-oriented UNsupervised Adversarial Network for Disease Recognition in Cross-Domain Chest X-rays

MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation

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

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

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

CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement

Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST

Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks

CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation

Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST

Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs

Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection