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首页> 外国专利> METHOD AND SYSTEM FOR INTRACEREBRAL HEMORRHAGE DETECTION AND SEGMENTATION BASED ON A MULTI-TASK FULLY CONVOLUTIONAL NETWORK

METHOD AND SYSTEM FOR INTRACEREBRAL HEMORRHAGE DETECTION AND SEGMENTATION BASED ON A MULTI-TASK FULLY CONVOLUTIONAL NETWORK

机译：基于多任务完全卷积网络的脑出血检测和分割方法和系统

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摘要

Embodiments of the disclosure provide systems and methods for detecting a medical condition of a subject. The system includes a communication interface configured to receive a sequence of images acquired from the subject by an image acquisition device and an end-to-end multi-task learning model. The end-to-end multi-task learning model includes an encoder, a Convolutional Recurrent Neural Network (ConvRNN), and at least one of a decoder and a classifier. The system further includes at least one processor configured to extract feature maps from the images using the encoder, capture contextual information between adjacent images in the sequence using the ConvRNN, and detect medical condition of the subject using the classifier based on the extracted feature maps of the image slices and the contextual information or segment each image slice using the decoder to obtain a region of interest indicative of the medical condition based on the extracted feature maps.
机译：本公开的实施例提供了用于检测受试者的医疗状况的系统和方法。 该系统包括通信接口，该通信接口被配置为通过图像获取设备接收从对象获取的图像序列和端到端的多任务学习模型。 端到端的多任务学习模型包括编码器，卷积经常性神经网络（CONNRNN）和解码器和分类器中的至少一个。 该系统还包括至少一个处理器，该处理器被配置为使用编码器从图像中提取特征映射，使用CONCRNN在序列中捕获相邻图像之间的上下文信息，并使用分类器基于所提取的特征映射检测对象的医疗状况。 使用解码器的图像切片和上下文信息或段每个图像切片基于提取的特征映射获得指示医疗条件的感兴趣区域。

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