Search Results for author: Ran Gu

Found 16 papers, 12 papers with code

UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation

1 code implementation19 Sep 2023 Jianghao Wu, Guotai Wang, Ran Gu, Tao Lu, Yinan Chen, Wentao Zhu, Tom Vercauteren, Sébastien Ourselin, Shaoting Zhang

The different predictions in these duplicated heads are used to obtain pseudo labels for unlabeled target-domain images and their uncertainty to identify reliable pseudo labels.

Brain Segmentation Image Segmentation +5

CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation

1 code implementation22 Nov 2022 Ran Gu, Guotai Wang, Jiangshan Lu, Jingyang Zhang, Wenhui Lei, Yinan Chen, Wenjun Liao, Shichuan Zhang, Kang Li, Dimitris N. Metaxas, Shaoting Zhang

First, a disentangle network is proposed to decompose an image into a domain-invariant anatomical representation and a domain-specific style code, where the former is sent to a segmentation model that is not affected by the domain shift, and the disentangle network is regularized by a decoder that combines the anatomical and style codes to reconstruct the input image.

Disentanglement Domain Generalization +4

PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation

1 code implementation19 Aug 2022 Guotai Wang, Xiangde Luo, Ran Gu, Shuojue Yang, Yijie Qu, Shuwei Zhai, Qianfei Zhao, Kang Li, Shaoting Zhang

Existing toolkits mainly focus on fully supervised segmentation and require full and accurate pixel-level annotations that are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the annotation cost.

Image Segmentation Medical Image Segmentation +3

Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures

1 code implementation18 Aug 2022 Ran Gu, Jingyang Zhang, Guotai Wang, Wenhui Lei, Tao Song, Xiaofan Zhang, Kang Li, Shaoting Zhang

To solve this problem, we propose Contrastive Semi-supervised learning for Cross Anatomy Domain Adaptation (CS-CADA) that adapts a model to segment similar structures in a target domain, which requires only limited annotations in the target domain by leveraging a set of existing annotated images of similar structures in a source domain.

Anatomy Contrastive Learning +4

Learning towards Synchronous Network Memorizability and Generalizability for Continual Segmentation across Multiple Sites

no code implementations14 Jun 2022 Jingyang Zhang, Peng Xue, Ran Gu, Yuning Gu, Mianxin Liu, Yongsheng Pan, Zhiming Cui, Jiawei Huang, Lei Ma, Dinggang Shen

In clinical practice, a segmentation network is often required to continually learn on a sequential data stream from multiple sites rather than a consolidated set, due to the storage cost and privacy restriction.

Continual Learning

Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation

1 code implementation13 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.

Disentanglement Domain Generalization +4

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 Image Segmentation +5

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 Image Segmentation +2

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.

Anatomy Coronary Artery Segmentation +2

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) Segmentation

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 +4

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

3 code implementations22 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.

Image Segmentation Lesion Segmentation +3

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