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CT lymph node detection system based on spatial-temporal recurrent attention mechanism

机译:CT淋巴结检测系统基于空间复发性注意力机制

摘要

The present disclosure discloses a CT lymph node detection system based on a spatial-temporal recurrent attention mechanism and specifically relates to the field of medical image analysis technologies. Based on a deep convolutional neural network and a recurrent attention mechanism, the present disclosure can construct an attention feature map adaptive to a lesion size in a slice direction and a spatial direction of a lymph node CT sequence. Firstly, a high-level spatial feature corresponding to the lymph node CT image is extracted by use of a pre-trained convolutional network; secondly, a recurrent attention mechanism based on a Gaussian Kernel Function is constructed with a slice at the center of the lymph node as a reference in a spatial domain; based on this, a temporal (slice direction) attention mechanism based on a Gaussian Mixture Model is performed; in addition, a predicted attention position is constrained based on the prior information of position distribution of the lymph node in the CT slice sequence; finally, in combination with the high-level features extracted by the two attention methods, the recurrent neural network performs classification to obtain a lymph node detection result.
机译:本公开公开了一种基于空间 - 时间复发机制的CT淋巴结检测系统,具体涉及医学图像分析技术领域。基于深度卷积神经网络和经常性注意机理,本公开可以构建适应性的注意特征图,其在切片方向上的病变尺寸和淋巴结CT序列的空间方向。首先,通过使用预先训练的卷积网络提取对应于淋巴结CT图像的高级空间特征;其次,基于高斯核函数的复发性注意机构用淋巴结中心的切片作为空间域中的参考;基于此,执行基于高斯混合模型的时间(切片方向)注意机构;另外,基于CT切片序列中淋巴结的位置分布的先前信息来限制预测的注意位置;最后,与由两个注意方法提取的高级特征结合,经常性神经网络执行分类以获得淋巴结检测结果。

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