Search Results for author: Jian Zhuang

Found 21 papers, 4 papers with code

Less is More: Surgical Phase Recognition from Timestamp Supervision

1 code implementation16 Feb 2022 Xinpeng Ding, Xinjian Yan, Zixun Wang, Wei Zhao, Jian Zhuang, Xiaowei Xu, Xiaomeng Li

Our study uncovers unique insights of surgical phase recognition with timestamp supervisions: 1) timestamp annotation can reduce 74% annotation time compared with the full annotation, and surgeons tend to annotate those timestamps near the middle of phases; 2) extensive experiments demonstrate that our method can achieve competitive results compared with full supervision methods, while reducing manual annotation cost; 3) less is more in surgical phase recognition, i. e., less but discriminative pseudo labels outperform full but containing ambiguous frames; 4) the proposed UATD can be used as a plug and play method to clean ambiguous labels near boundaries between phases, and improve the performance of the current surgical phase recognition methods.

Surgical phase recognition

"One-Shot" Reduction of Additive Artifacts in Medical Images

no code implementations23 Oct 2021 Yu-Jen Chen, Yen-Jung Chang, Shao-Cheng Wen, Yiyu Shi, Xiaowei Xu, Tsung-Yi Ho, Meiping Huang, Haiyun Yuan, Jian Zhuang

Medical images may contain various types of artifacts with different patterns and mixtures, which depend on many factors such as scan setting, machine condition, patients' characteristics, surrounding environment, etc.

Computed Tomography (CT)

Segmentation with Multiple Acceptable Annotations: A Case Study of Myocardial Segmentation in Contrast Echocardiography

2 code implementations29 Jun 2021 Dewen Zeng, Mingqi Li, Yukun Ding, Xiaowei Xu, Qiu Xie, Ruixue Xu, Hongwen Fei, Meiping Huang, Jian Zhuang, Yiyu Shi

Experiment results on our clinical MCE data set demonstrate that the neural network trained with the proposed loss function outperforms those existing ones that try to obtain a unique ground truth from multiple annotations, both quantitatively and qualitatively.

Image Segmentation Segmentation +1

EchoCP: An Echocardiography Dataset in Contrast Transthoracic Echocardiography for Patent Foramen Ovale Diagnosis

no code implementations18 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.

Quantization of Deep Neural Networks for Accurate Edge Computing

no code implementations25 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

ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease

1 code implementation26 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.

Classification Computed Tomography (CT) +1

Attention Based Joint Learning for Supervised Premature Ventricular Contraction Differentiation with Unsupervised Abnormal Beat Segmentation

no code implementations1 Jan 2021 Xinrong Hu, long wen, shushui wang, Dongpo Liang, Jian Zhuang, Yiyu Shi

Though the training data is only labeled to supervise theclassifier, the segmenter and the classifier are trained in an end-to-end manner sothat optimizing classification performance also adjusts how the abnormal beats aresegmented.

Classification General Classification

Myocardial Segmentation of Cardiac MRI Sequences with Temporal Consistency for Coronary Artery Disease Diagnosis

no code implementations29 Dec 2020 Yutian Chen, Xiaowei Xu, Dewen Zeng, Yiyu Shi, Haiyun Yuan, Jian Zhuang, Yuhao Dong, Qianjun Jia, Meiping Huang

Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial segmentation of Magnetic Resonance Imaging (MRI) sequences.


Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation

no code implementations17 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.

MRI segmentation Neural Architecture Search +1

Multi-Cycle-Consistent Adversarial Networks for CT Image Denoising

no code implementations27 Feb 2020 Jinglan Liu, Yukun Ding, JinJun Xiong, Qianjun Jia, Meiping Huang, Jian Zhuang, Bike Xie, Chun-Chen Liu, Yiyu Shi

For example, if the noise is large leading to significant difference between domain $X$ and domain $Y$, can we bridge $X$ and $Y$ with an intermediate domain $Z$ such that both the denoising process between $X$ and $Z$ and that between $Z$ and $Y$ are easier to learn?

Image Denoising Image-to-Image Translation +1

Uncertainty-Aware Training of Neural Networks for Selective Medical Image Segmentation

no code implementations MIDL 2019 Yukun Ding, Jinglan Liu, Xiaowei Xu, Meiping Huang, Jian Zhuang, JinJun Xiong, Yiyu Shi

Existing selective segmentation methods, however, ignore this unique property of selective segmentation and train their DNN models by optimizing accuracy on the entire dataset.

Image Segmentation Medical Image Segmentation +2

Accurate Congenital Heart Disease Model Generation for 3D Printing

no code implementations6 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.

Anatomy Decision Making +2

Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds

no code implementations CVPR 2019 Zihao Liu, Xiaowei Xu, Tao Liu, Qi Liu, Yanzhi Wang, Yiyu Shi, Wujie Wen, Meiping Huang, Haiyun Yuan, Jian Zhuang

In this paper we will use deep learning based medical image segmentation as a vehicle and demonstrate that interestingly, machine and human view the compression quality differently.

Image Compression Image Segmentation +3

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