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TOWARDS UNBIASED COVID-19 LESION LOCALISATION AND SEGMENTATION VIA WEAKLY SUPERVISED LEARNING Despite tremendous efforts, it is very challenging to generate a robust model to assist in the accurate quantification assessment of COVID-19 on chest CT images. Due to the nature of blurred boundaries, the supervised segmentation methods usually suffer from annotation biases. To support unbiased lesion localisation and to minimise the labeling costs, we propose a data-driven framework supervised by only image-level labels. The framework can explicitly separate potential lesions from original images, with the help of a generative adversarial network and a lesion-specific decoder. Experiments on two COVID-19 datasets demonstrate the effectiveness of the proposed framework and its superior performance to several existing methods.

Updated 2 days ago

TOWARDS UNBIASED COVID-19 LESION LOCALISATION AND SEGMENTATION VIA WEAKLY SUPERVISED LEARNING Despite tremendous efforts, it is very challenging to generate a robust model to assist in the accurate quantification assessment of COVID-19 on chest CT images. Due to the nature of blurred boundaries, the supervised segmentation methods usually suffer from annotation biases. To support unbiased lesion localisation and to minimise the labeling costs, we propose a data-driven framework supervised by only image-level labels. The framework can explicitly separate potential lesions from original images, with the help of a generative adversarial network and a lesion-specific decoder. Experiments on two COVID-19 datasets demonstrate the effectiveness of the proposed framework and its superior performance to several existing methods.

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PanGu-Alpha 模型在 GPU 上推理和训练-测试

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octopus

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大数据处理平台

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面向多计算场景的一站式融合计算平台

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郑州飞桨活动案例集

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本项目是群体化方法与技术的开源实现案例,在基于Gitea的基础上,进一步支持社交化的协同开发、协同学习、协同研究等群体创新实践服务,特别是针对新一代人工智能技术特点,重点支持项目管理、git代码管理、大数据集存储管理与智能计算平台接入。

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PanGu-Alpha 模型在 GPU 上推理和训练

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