Search Results for author: Xiuquan Du

Found 4 papers, 0 papers with code

AGMR-Net: Attention Guided Multiscale Recovery framework for stroke segmentation

no code implementations28 Feb 2022 Xiuquan Du, Kunpeng Ma, Yuhui Song

Firstly, a coarse-grained patch attention module in the encoding is adopted to get a patch-based coarse-grained attention map in a multi-stage explicitly supervised way, enabling target spatial context saliency representation with a patch-based weighting technique that eliminates the effect of intra-class inconsistency.

Lesion Segmentation

Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention

no code implementations2 Feb 2020 Guang Yang, Jun Chen, Zhifan Gao, Shuo Li, Hao Ni, Elsa Angelini, Tom Wong, Raad Mohiaddin, Eva Nyktari, Ricardo Wage, Lei Xu, Yanping Zhang, Xiuquan Du, Heye Zhang, David Firmin, Jennifer Keegan

Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0. 27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices).

Anatomy Segmentation

Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation

no code implementations12 Jun 2018 Jun Chen, Guang Yang, Zhifan Gao, Hao Ni, Elsa Angelini, Raad Mohiaddin, Tom Wong, Yanping Zhang, Xiuquan Du, Heye Zhang, Jennifer Keegan, David Firmin

Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success.

Anatomy Segmentation

Direct detection of pixel-level myocardial infarction areas via a deep-learning algorithm

no code implementations10 Jun 2017 Chenchu Xu, Lei Xu, Zhifan Gao, Shen zhao, Heye Zhang, Yanping Zhang, Xiuquan Du, Shu Zhao, Dhanjoo Ghista, Shuo Li

Accurate detection of the myocardial infarction (MI) area is crucial for early diagnosis planning and follow-up management.

Management Time Series Analysis

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