no code implementations • 11 Jun 2018 • Dakai Jin, Ziyue Xu, You-Bao Tang, Adam P. Harrison, Daniel J. Mollura
Qualitative results demonstrate the effectiveness of our method compared to the state-of-art.
no code implementations • 25 Jan 2018 • Jinzheng Cai, You-Bao Tang, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers
Toward this end, we introduce a convolutional neural network based weakly supervised self-paced segmentation (WSSS) method to 1) generate the initial lesion segmentation on the axial RECIST-slice; 2) learn the data distribution on RECIST-slices; 3) adapt to segment the whole volume slice by slice to finally obtain a volumetric segmentation.
no code implementations • 2 Jul 2018 • Jinzheng Cai, You-Bao Tang, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers
Volumetric lesion segmentation from computed tomography (CT) images is a powerful means to precisely assess multiple time-point lesion/tumor changes.
no code implementations • 5 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.
no code implementations • 21 Aug 2019 • Yu-Xing Tang, You-Bao Tang, Veit Sandfort, Jing Xiao, Ronald M. Summers
In this work, we exploit the unsupervised domain adaptation problem for radiology image interpretation across domains.
Generative Adversarial Network Image-to-Image Translation +2
no code implementations • 23 Jan 2020 • Vatsal Agarwal, You-Bao Tang, Jing Xiao, Ronald M. Summers
Lesion segmentation on computed tomography (CT) scans is an important step for precisely monitoring changes in lesion/tumor growth.
no code implementations • 24 Jan 2020 • Vatsal Agarwal, You-Bao Tang, Jing Xiao, Ronald M. Summers
In this work, we propose a weakly-supervised co-segmentation model that first generates pseudo-masks from the RECIST slices and uses these as training labels for an attention-based convolutional neural network capable of segmenting common lesions from a pair of CT scans.
no code implementations • 8 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.
no code implementations • 14 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.
14 code implementations • 12 Aug 2019 • Ke Yan, You-Bao Tang, Yifan Peng, Veit Sandfort, Mohammadhadi Bagheri, Zhiyong Lu, Ronald M. Summers
When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report.
Ranked #7 on Medical Object Detection on DeepLesion