Search Results for author: Zhengqiang Zhang

Found 5 papers, 1 papers with code

FakeMix Augmentation Improves Transparent Object Detection

1 code implementation24 Mar 2021 Yang Cao, Zhengqiang Zhang, Enze Xie, Qibin Hou, Kai Zhao, Xiangui Luo, Jian Tuo

However, these methods usually encounter boundary-related imbalance problem, leading to limited generation capability.

Data Augmentation object-detection +2

Closed-Loop Adaptation for Weakly-Supervised Semantic Segmentation

no code implementations29 May 2019 Zhengqiang Zhang, Shujian Yu, Shi Yin, Qinmu Peng, Xinge You

Weakly-supervised semantic segmentation aims to assign each pixel a semantic category under weak supervisions, such as image-level tags.

Superpixels Weakly-Supervised Semantic Segmentation

Fast and accurate reconstruction of HARDI using a 1D encoder-decoder convolutional network

no code implementations21 Mar 2019 Shi Yin, Zhengqiang Zhang, Qinmu Peng, Xinge You

High angular resolution diffusion imaging (HARDI) demands a lager amount of data measurements compared to diffusion tensor imaging, restricting its use in practice.

Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network

no code implementations5 Jan 2019 Shi Yin, Zhengqiang Zhang, Hongming Li, Qinmu Peng, Xinge You, Susan L. Furth, Gregory E. Tasian, Yong Fan

It remains challenging to automatically segment kidneys in clinical ultrasound images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance.

Classification General Classification

Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks

no code implementations12 Nov 2018 Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Susan L. Furth, Gregory E. Tasian, Yong Fan

It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance.

Classification Data Augmentation +1

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