no code implementations • 17 Aug 2021 • Chi-Tung Cheng, Jinzheng Cai, Wei Teng, Youjing Zheng, YuTing Huang, Yu-Chao Wang, Chien-Wei Peng, YouBao Tang, Wei-Chen Lee, Ta-Sen Yeh, Jing Xiao, Le Lu, Chien-Hung Liao, Adam P. Harrison
We develop a flexible three-dimensional deep algorithm, called hetero-phase volumetric detection (HPVD), that can accept any combination of contrast-phase inputs and with adjustable sensitivity depending on the clinical purpose.
no code implementations • 5 May 2021 • YouBao Tang, Ke Yan, Jinzheng Cai, Lingyun Huang, Guotong Xie, Jing Xiao, JingJing Lu, Gigin Lin, Le Lu
PDNet learns comprehensive and representative deep image features for our tasks and produces more accurate results on both lesion segmentation and RECIST diameter prediction.
no code implementations • 3 May 2021 • YouBao Tang, Jinzheng Cai, Ke Yan, Lingyun Huang, Guotong Xie, Jing Xiao, JingJing Lu, Gigin Lin, Le Lu
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 universal lesion segmentation (ULS).
no code implementations • 19 Mar 2021 • Sicheng Zhao, Quanwei Huang, YouBao Tang, Xingxu Yao, Jufeng Yang, Guiguang Ding, Björn W. Schuller
Recently, extensive research efforts have been dedicated to understanding the emotions of images.
no code implementations • 9 Mar 2021 • Jieneng Chen, Ke Yan, Yu-Dong Zhang, YouBao Tang, Xun Xu, Shuwen Sun, Qiuping Liu, Lingyun Huang, Jing Xiao, Alan L. Yuille, Ya zhang, Le Lu
(2) The sampled deep vertex features with positional embedding are mapped into a sequential space and decoded by a multilayer perceptron (MLP) for semantic classification.
1 code implementation • CVPR 2021 • Jinzheng Cai, YouBao Tang, Ke Yan, Adam P. Harrison, Jing Xiao, Gigin Lin, Le Lu
In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals.
no code implementations • 4 Dec 2020 • Ke Yan, Jinzheng Cai, Dakai Jin, Shun Miao, Dazhou Guo, Adam P. Harrison, YouBao Tang, Jing Xiao, JingJing Lu, Le Lu
We introduce such an approach, called Self-supervised Anatomical eMbedding (SAM).
1 code implementation • 5 Sep 2020 • Ke Yan, Jinzheng Cai, Youjing Zheng, Adam P. Harrison, Dakai Jin, YouBao Tang, Yuxing Tang, Lingyun Huang, Jing Xiao, Le Lu
For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations).
no code implementations • 21 Jul 2020 • Youbao Tang, Ke Yan, Jing Xiao, Ranold M. Summers
Based on the results of the first network, the second one refines the lesion segmentation and RECIST estimation.
no code implementations • 19 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.
2 code implementations • 19 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.
Ranked #1 on
Lung Nodule Segmentation
on NIH
1 code implementation • 18 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.
no code implementations • 18 Jul 2018 • Youbao Tang, Jinzheng Cai, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers
The first GAN reduces the noise in the CT image and the second GAN generates a higher resolution image with enhanced boundaries and high contrast.
no code implementations • 25 Jun 2018 • Youbao Tang, Adam P. Harrison, Mohammadhadi Bagheri, Jing Xiao, Ronald M. Summers
Response evaluation criteria in solid tumors (RECIST) is the standard measurement for tumor extent to evaluate treatment responses in cancer patients.
no code implementations • 18 Aug 2016 • Youbao Tang, Xiangqian Wu, Wei Bu
This paper proposes a novel saliency detection method by developing a deeply-supervised recurrent convolutional neural network (DSRCNN), which performs a full image-to-image saliency prediction.
no code implementations • 18 Aug 2016 • Youbao Tang, Xiangqian Wu
This paper proposes a novel saliency detection method by combining region-level saliency estimation and pixel-level saliency prediction with CNNs (denoted as CRPSD).