Titlebook: Left Atrial and Scar Quantification and Segmentation; First Challenge, LAS Xiahai Zhuang,Lei Li,Fuping Wu Conference proceedings 2023 The E

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,Self Pre-training with?Single-Scale Adapter for?Left Atrial Segmentation,erform the pretext task of reconstructing the original MRI images from only partial patches, where the ViT encoder is encouraged to learn contextual information as priors by aggregating global information to recover the contents in masked patches. In the fine-tuning process, we further propose an si

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,Deep U-Net Architecture with?Curriculum Learning for?Left Atrial Segmentation,an reduce the domain shifts and achieve more accurate result, with mean Dice score of 0.881 on the validation set of LAScarQS 2022 challenge. The evaluation results demonstrate our method’s effectiveness on left atrial segmentation in multi-sequence cardiac magnetic resonance (CMR) data.

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Cross-Domain Segmentation of Left Atrium Based on Multi-scale Decision Level Fusion,s of LAScarQS 2022 challenge for training and 20 cases without labels which also provided by them for testing. We uploaded the test results to the online test platform, which allowed each team to test up to 10 times, and the best result we achieved was Dice 0.88314, Hausdorff 20.88313mm and ASSD 1.7

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