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MEDICAL IMAGE SEGMENTATION USING DEEP LEARNING MODELS TRAINED WITH RANDOM DROPOUT AND/OR STANDARDIZED INPUTS
MEDICAL IMAGE SEGMENTATION USING DEEP LEARNING MODELS TRAINED WITH RANDOM DROPOUT AND/OR STANDARDIZED INPUTS
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机译:使用经过随机抽取和/或标准化输入训练的深度学习模型对医学图像进行分段
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摘要
Systems and methods are described for segmenting medical images, such as magnetic resonance images, using a deep learning model that has been trained using random dropped inputs, standardized inputs, or both. Medical images can be segmented based on anatomy, physiology, pathology, other properties or characteristics represented in the medical images, or combinations thereof. As one example, multi-contrast magnetic resonance images are input to the trained deep learning model in order to generate multiple segmented medical images, each representing a different segmentation class.
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