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20162022
most citedCT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation

24 citations · 86 across the 22 of their papers we have counts for

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Showing eess.IVShow all

7 papers · 1 filter

eess.IV2022★ 2 cited

Accurate and Robust Lesion RECIST Diameter Prediction and Segmentation with Transformers

Youbao Tang, Ning Zhang, Yirui Wang +4

Automatically measuring lesion/tumor size with RECIST (Response Evaluation Criteria In Solid Tumors) diameters and segmentation is important for computer-aided diagnosis. Although…

eess.IV2021

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

Youbao Tang, Ke Yan, Jinzheng Cai +6

Measuring lesion size is an important step to assess tumor growth and monitor disease progression and therapy response in oncology image analysis. Although it is tedious and highly…

eess.IV2021★ 3 cited

Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss

Youbao Tang, Jinzheng Cai, Ke Yan +6

Accurately segmenting a variety of clinically significant lesions from whole body computed tomography (CT) scans is a critical task on precision oncology imaging, denoted as univer…

eess.IV2020★ 1 cited

One Click Lesion RECIST Measurement and Segmentation on CT Scans

Youbao Tang, Ke Yan, Jing Xiao +1

In clinical trials, one of the radiologists' routine work is to measure tumor sizes on medical images using the RECIST criteria (Response Evaluation Criteria In Solid Tumors). Howe…

eess.IV2020★ 6 cited

ENet: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans

Youbao Tang, Yuxing Tang, Yingying Zhu +2

Developing an effective liver and liver tumor segmentation model from CT scans is very important for the success of liver cancer diagnosis, surgical planning and cancer treatment.…

eess.IV2020★ 4 cited

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

Yingying Zhu, Youbao Tang, Yuxing Tang +4

Current deep learning based segmentation models often generalize poorly between domains due to insufficient training data. In real-world clinical applications, cross-domain image a…

eess.IV2020★ 3 cited

Bone Suppression on Chest Radiographs With Adversarial Learning

Jia Liang, Yuxing Tang, Youbao Tang +2

Dual-energy (DE) chest radiography provides the capability of selectively imaging two clinically relevant materials, namely soft tissues, and osseous structures, to better characte…