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Detecting intratumor heterogeneity of molecular subtypes in pathology slide images using deep-learning
Detecting intratumor heterogeneity of molecular subtypes in pathology slide images using deep-learning
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机译:使用深度学习检测病理幻灯片图像中分子亚型的肿瘤内异质性
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摘要
Techniques are provided for determining molecular subtype classifications based on pathology slide images (SIs). A plurality of training SIs is segmented into a plurality of scaled patches. Each scaled patch is converted into a multiscale descriptor using a deep-learning neural network by mapping each of one or more patch representations to a patch-level descriptor and combining the patch-level descriptors. A classifier model is configured and trained to process the multiscale descriptors such that, for each training SI, the classifier model is operable to assign a patch-level molecular subtype classification to each of the scaled patches corresponding to the training SI and determine a Si-level molecular subtype classification based on the patch-level molecular subtype classifications. A molecular subtyping engine is configured to use the trained classifier model to determine a SI-level molecular subtype classification for a test SI.
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