no code implementations • 13 May 2023 • Pirazh Khorramshahi, Zhe Wu, Tianchen Wang, Luke DeLuccia, Hongcheng Wang
Despite recent advances in video-based action recognition and robust spatio-temporal modeling, most of the proposed approaches rely on the abundance of computational resources to afford running huge and computation-intensive convolutional or transformer-based neural networks to obtain satisfactory results.
no code implementations • 3 Nov 2022 • An Zeng, Chunbiao Wu, Meiping Huang, Jian Zhuang, Shanshan Bi, Dan Pan, Najeeb Ullah, Kaleem Nawaz Khan, Tianchen Wang, Yiyu Shi, Xiaomeng Li, Guisen Lin, Xiaowei Xu
In this paper, we propose a large-scale dataset for coronary artery segmentation on CTA images.
no code implementations • 1 Sep 2021 • Zeyang Yao, Jiawei Zhang, Hailong Qiu, Tianchen Wang, Yiyu Shi, Jian Zhuang, Yuhao Dong, Meiping Huang, Xiaowei Xu
Results show that the baseline method can achieve comparable results with existing works on aorta and TL segmentation.
no code implementations • 18 May 2021 • Tianchen Wang, Zhihe Li, Meiping Huang, Jian Zhuang, Shanshan Bi, Jiawei Zhang, Yiyu Shi, Hongwen Fei, Xiaowei Xu
For PFO diagnosis, contrast transthoracic echocardiography (cTTE) is preferred as being a more robust method compared with others.
no code implementations • 25 Apr 2021 • Wentao Chen, Hailong Qiu, Jian Zhuang, Chutong Zhang, Yu Hu, Qing Lu, Tianchen Wang, Yiyu Shi, Meiping Huang, Xiaowe Xu
Deep neural networks (DNNs) have demonstrated their great potential in recent years, exceeding the per-formance of human experts in a wide range of applications.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • 25 Apr 2021 • Xiaowe Xu, Jiawei Zhang, Jinglan Liu, Yukun Ding, Tianchen Wang, Hailong Qiu, Haiyun Yuan, Jian Zhuang, Wen Xie, Yuhao Dong, Qianjun Jia, Meiping Huang, Yiyu Shi
As one of the most commonly ordered imaging tests, computed tomography (CT) scan comes with inevitable radiation exposure that increases the cancer risk to patients.
1 code implementation • 26 Jan 2021 • Xiaowei Xu, Tianchen Wang, Jian Zhuang, Haiyun Yuan, Meiping Huang, Jianzheng Cen, Qianjun Jia, Yuhao Dong, Yiyu Shi
To demonstrate this, we further present a baseline framework for the automatic classification of CHD, based on a state-of-the-art CHD segmentation method.
no code implementations • 17 Aug 2020 • Dewen Zeng, Weiwen Jiang, Tianchen Wang, Xiaowei Xu, Haiyun Yuan, Meiping Huang, Jian Zhuang, Jingtong Hu, Yiyu Shi
Experimental results on ACDC MICCAI 2017 dataset demonstrate that our hardware-aware multi-scale NAS framework can reduce the latency by up to 3. 5 times and satisfy the real-time constraints, while still achieving competitive segmentation accuracy, compared with the state-of-the-art NAS segmentation framework.
no code implementations • 18 Jul 2020 • Tianchen Wang, Xiaowei Xu, JinJun Xiong, Qianjun Jia, Haiyun Yuan, Meiping Huang, Jian Zhuang, Yiyu Shi
Real-time cine magnetic resonance imaging (MRI) plays an increasingly important role in various cardiac interventions.
no code implementations • CVPR 2020 • Song Bian, Tianchen Wang, Masayuki Hiromoto, Yiyu Shi, Takashi Sato
In this work, we propose ENSEI, a secure inference (SI) framework based on the frequency-domain secure convolution (FDSC) protocol for the efficient execution of privacy-preserving visual recognition.
no code implementations • 15 Sep 2019 • Tianchen Wang, JinJun Xiong, Xiaowei Xu, Meng Jiang, Yiyu Shi, Haiyun Yuan, Meiping Huang, Jian Zhuang
Cardiac magnetic resonance imaging (MRI) is an essential tool for MRI-guided surgery and real-time intervention.
no code implementations • 6 Jul 2019 • Xiaowei Xu, Tianchen Wang, Dewen Zeng, Yiyu Shi, Qianjun Jia, Haiyun Yuan, Meiping Huang, Jian Zhuang
3D printing has been widely adopted for clinical decision making and interventional planning of Congenital heart disease (CHD), while whole heart and great vessel segmentation is the most significant but time-consuming step in the model generation for 3D printing.
no code implementations • 15 Mar 2019 • Tianchen Wang, JinJun Xiong, Xiaowei Xu, Yiyu Shi
By introducing a parameterized canonical model to model correlated data and defining corresponding operations as required for CNN training and inference, we show that SCNN can process multiple frames of correlated images effectively, hence achieving significant speedup over existing CNN models.