Search Results for author: Ran Gu

Found 11 papers, 7 papers with code

Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation

no code implementations13 May 2022 Ran Gu, Jiangshan Lu, Jingyang Zhang, Wenhui Lei, Xiaofan Zhang, Guotai Wang, Shaoting Zhang

To tackle this deficiency, we propose Contrastive Domain Disentangle (CDD) network for generalizable medical image segmentation.

One-shot Weakly-Supervised Segmentation in Medical Images

1 code implementation21 Nov 2021 Wenhui Lei, Qi Su, Ran Gu, Na Wang, Xinglong Liu, Guotai Wang, Xiaofan Zhang, Shaoting Zhang

Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation.

Denoising Medical Image Segmentation +2

Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation

1 code implementation18 Sep 2021 Ran Gu, Jingyang Zhang, Rui Huang, Wenhui Lei, Guotai Wang, Shaoting Zhang

First, we present a domain composition method that represents one certain domain by a linear combination of a set of basis representations (i. e., a representation bank).

Domain Generalization Medical Image Segmentation +1

SS-CADA: A Semi-Supervised Cross-Anatomy Domain Adaptation for Coronary Artery Segmentation

no code implementations6 May 2021 Jingyang Zhang, Ran Gu, Guotai Wang, Hongzhi Xie, Lixu Gu

To solve this problem, we propose a Semi-Supervised Cross-Anatomy Domain Adaptation (SS-CADA) which requires only limited annotations for coronary arteries in XAs.

Domain Adaptation

Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss

1 code implementation3 Feb 2021 Wenhui Lei, Haochen Mei, Zhengwentai Sun, Shan Ye, Ran Gu, Huan Wang, Rui Huang, Shichuan Zhang, Shaoting Zhang, Guotai Wang

Despite the stateof-the-art performance achieved by Convolutional Neural Networks (CNNs) for automatic segmentation of OARs, existing methods do not provide uncertainty estimation of the segmentation results for treatment planning, and their accuracy is still limited by several factors, including the low contrast of soft tissues in CT, highly imbalanced sizes of OARs and large inter-slice spacing.

Computed Tomography (CT)

A fast two-stage algorithm for non-negative matrix factorization in streaming data

no code implementations21 Jan 2021 Ran Gu, Qiang Du, Simon J. L. Billinge

In the second stage, an interior point method is adopted to accelerate the local convergence.

Optimization and Control Numerical Analysis Numerical Analysis 65K10, 90C26 G.1.6; F.2.1

Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images

2 code implementations13 Dec 2020 Wenhui Lei, Wei Xu, Ran Gu, Hao Fu, Shaoting Zhang, Guotai Wang

To address this problem, we present a one-shot framework for organ and landmark localization in volumetric medical images, which does not need any annotation during the training stage and could be employed to locate any landmarks or organs in test images given a support (reference) image during the inference stage.

Contrastive Learning Object Localization +1

The smallest number of vertices in a 2-arc-strong digraph which has no good pair

no code implementations7 Dec 2020 Ran Gu, Gregory Gutin, Shasha Li, Yongtang Shi, Zhenyu Taoqiu

They also proved that every digraph on at most 6 vertices and arc-connectivity at least 2 has a good pair and gave an example of a 2-arc-strong digraph $D$ on 10 vertices with independence number 4 that has no good pair.


CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

1 code implementation22 Sep 2020 Ran Gu, Guotai Wang, Tao Song, Rui Huang, Michael Aertsen, Jan Deprest, Sébastien Ourselin, Tom Vercauteren, Shaoting Zhang

Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object.

Lesion Segmentation Semantic Segmentation +1

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