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20182025
most citedOn Neural Architecture Search for Resource-Constrained Hardware Platforms

59 citations · 163 across the 26 of their papers we have counts for

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Showing 2021Show all

10 papers · 1 filter

eess.IV2021

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

Yu-Jen Chen, Yen-Jung Chang, Shao-Cheng Wen +6

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' characte…

cs.CV2021★ 2 cited

Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation

Xinrong Hu, Dewen Zeng, Xiaowei Xu +1

The success of deep learning methods in medical image segmentation tasks heavily depends on a large amount of labeled data to supervise the training. On the other hand, the annotat…

eess.IV2021

Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography

Dewen Zeng, Yukun Ding, Haiyun Yuan +5

Automatic myocardial segmentation of contrast echocardiography has shown great potential in the quantification of myocardial perfusion parameters. Segmentation quality control is a…

eess.IV2021★ 3 cited

ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection

Zeyang Yao, Jiawei Zhang, Hailong Qiu +6

Type-B Aortic Dissection (TBAD) is one of the most serious cardiovascular events characterized by a growing yearly incidence,and the severity of disease prognosis. Currently, compu…

cs.CV2021

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

Dewen Zeng, Mingqi Li, Yukun Ding +7

Most existing deep learning-based frameworks for image segmentation assume that a unique ground truth is known and can be used for performance evaluation. This is true for many app…

cs.CV2021★ 6 cited

Positional Contrastive Learning for Volumetric Medical Image Segmentation

Dewen Zeng, Yawen Wu, Xinrong Hu +6

The success of deep learning heavily depends on the availability of large labeled training sets. However, it is hard to get large labeled datasets in medical image domain because o…

eess.IV2021

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

Tianchen Wang, Zhihe Li, Meiping Huang +6

Patent foramen ovale (PFO) is a potential separation between the septum, primum and septum secundum located in the anterosuperior portion of the atrial septum. PFO is one of the ma…

eess.IV2021

Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images

Xiaowe Xu, Jiawei Zhang, Jinglan Liu +10

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. However, CT im…

cs.CV2021★ 4 cited

Quantization of Deep Neural Networks for Accurate Edge Computing

Wentao Chen, Hailong Qiu, Jian Zhuang +7

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. Due to their large…

eess.IV2021★ 1 cited

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

Xiaowei Xu, Tianchen Wang, Jian Zhuang +6

Congenital heart disease (CHD) is the most common type of birth defect, which occurs 1 in every 110 births in the United States. CHD usually comes with severe variations in heart s…