Search Results for author: Qiangguo Jin

Found 6 papers, 5 papers with code

RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

1 code implementation4 Nov 2018 Qiangguo Jin, Zhaopeng Meng, Changming Sun, Leyi Wei, Ran Su

Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes.

Brain Tumor Segmentation Deep Attention +3

Prototype as Query for Few Shot Semantic Segmentation

1 code implementation27 Nov 2022 Leilei Cao, Yibo Guo, Ye Yuan, Qiangguo Jin

In this way, the spatial details can be better captured and the semantic features of target class in the query image can be focused.

Few-Shot Semantic Segmentation

Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images

1 code implementation20 Apr 2021 Qiangguo Jin, Hui Cui, Changming Sun, Zhaopeng Meng, Leyi Wei, Ran Su

DASC-Net consists of a novel attention and feature domain enhanced domain adaptation model (AFD-DA) to solve the domain shifts and a self-correction learning process to refine segmentation results.

Domain Adaptation Segmentation

Free-form tumor synthesis in computed tomography images via richer generative adversarial network

1 code implementation20 Apr 2021 Qiangguo Jin, Hui Cui, Changming Sun, Zhaopeng Meng, Ran Su

The network is composed of a new richer convolutional feature enhanced dilated-gated generator (RicherDG) and a hybrid loss function.

Computed Tomography (CT) Generative Adversarial Network

Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation

1 code implementation19 Mar 2024 Qiangguo Jin, Hui Cui, Changming Sun, Yang song, Jiangbin Zheng, Leilei Cao, Leyi Wei, Ran Su

To address these issues, we first propose a novel inter- and intra-uncertainty regularization method to measure and constrain both inter- and intra-inconsistencies in the teacher-student architecture.

Image Segmentation Semantic Segmentation

DUNet: A deformable network for retinal vessel segmentation

no code implementations3 Nov 2018 Qiangguo Jin, Zhaopeng Meng, Tuan D. Pham, Qi Chen, Leyi Wei, Ran Su

Results show that more detailed vessels are extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0. 9697/0. 9722/0. 9724 and AUC of 0. 9856/0. 9868/0. 9863 on DRIVE, STARE and CHASE_DB1 respectively.

Retinal Vessel Segmentation Segmentation

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