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Anomaly detection in volumetric images using sequential convolutional and recurrent neural networks
Anomaly detection in volumetric images using sequential convolutional and recurrent neural networks
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机译：使用顺序卷积和递归神经网络的体积图像异常检测
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摘要
Computer-implemented methods and apparatuses for anomaly detection in volumetric images are provided. A two-dimensional convolutional neural network (CNN) is used to encode slices within a volumetric image, such as a CT scan. The CNN may be trained using an output layer that is subsequently omitted during use of the CNN as an encoder. The CNN encoder output is applied to a recurrent neural network (RNN), such as a long short-term memory network. The RNN may output various indications of the presence, probability and/or location of anomalies within the volumetric image.
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