Search Results for author: Zhong Xue

Found 9 papers, 3 papers with code

Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning

1 code implementation21 Nov 2021 Zheren Li, Zhiming Cui, Sheng Wang, Yuji Qi, Xi Ouyang, Qitian Chen, Yuezhi Yang, Zhong Xue, Dinggang Shen, Jie-Zhi Cheng

Specifically, the backbone network is firstly trained with a multi-style and multi-view unsupervised self-learning scheme for the embedding of invariant features to various vendor-styles.

Contrastive Learning Domain Generalization +2

mr2NST: Multi-Resolution and Multi-Reference Neural Style Transfer for Mammography

no code implementations25 May 2020 Sheng Wang, Jiayu Huo, Xi Ouyang, Jifei Che, Xuhua Ren, Zhong Xue, Qian Wang, Jie-Zhi Cheng

However, the image styles of different vendors are very distinctive, and there may exist domain gap among different vendors that could potentially compromise the universal applicability of one deep learning model.

Lesion Detection Style Transfer

A Self-ensembling Framework for Semi-supervised Knee Cartilage Defects Assessment with Dual-Consistency

1 code implementation19 May 2020 Jiayu Huo, Liping Si, Xi Ouyang, Kai Xuan, Weiwu Yao, Zhong Xue, Qian Wang, Dinggang Shen, Lichi Zhang

With dual-consistency checking of the attention in the lesion classification and localization, the two networks can gradually optimize the attention distribution and improve the performance of each other, whereas the training relies on partially labeled data only and follows the semi-supervised manner.

Classification General Classification +2

Reducing Magnetic Resonance Image Spacing by Learning Without Ground-Truth

no code implementations27 Mar 2020 Kai Xuan, Liping Si, Lichi Zhang, Zhong Xue, Yining Jiao, Weiwu Yao, Dinggang Shen, Dijia Wu, Qian Wang

In this work, we propose a novel deep-learning-based super-resolution algorithm to generate high-resolution (HR) MR images with small slice spacing from low-resolution (LR) inputs of large slice spacing.

Super-Resolution

Lung Infection Quantification of COVID-19 in CT Images with Deep Learning

1 code implementation10 Mar 2020 Fei Shan, Yaozong Gao, Jun Wang, Weiya Shi, Nannan Shi, Miaofei Han, Zhong Xue, Dinggang Shen, Yuxin Shi

The performance of the system was evaluated by comparing the automatically segmented infection regions with the manually-delineated ones on 300 chest CT scans of 300 COVID-19 patients.

COVID-19 Image Segmentation Segmentation

Synthesis and Inpainting-Based MR-CT Registration for Image-Guided Thermal Ablation of Liver Tumors

no code implementations30 Jul 2019 Dongming Wei, Sahar Ahmad, Jiayu Huo, Wen Peng, Yunhao Ge, Zhong Xue, Pew-Thian Yap, Wentao Li, Dinggang Shen, Qian Wang

Then, an unsupervised registration network is used to efficiently align the pre-procedural CT (pCT) with the inpainted iCT (inpCT) image.

Image Registration

Deep Learning based Inter-Modality Image Registration Supervised by Intra-Modality Similarity

no code implementations28 Apr 2018 Xiaohuan Cao, Jianhua Yang, Li Wang, Zhong Xue, Qian Wang, Dinggang Shen

In this paper, we propose to train a non-rigid inter-modality image registration network, which can directly predict the transformation field from the input multimodal images, such as CT and MR images.

Image Registration

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