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SYSTEMS, METHODS, AND APPARATUSES FOR TRAINING A DEEP MODEL TO LEARN CONTRASTIVE REPRESENTATIONS EMBEDDED WITHIN PART-WHOLE SEMANTICS VIA A SELF-SUPERVISED LEARNING FRAMEWORK
SYSTEMS, METHODS, AND APPARATUSES FOR TRAINING A DEEP MODEL TO LEARN CONTRASTIVE REPRESENTATIONS EMBEDDED WITHIN PART-WHOLE SEMANTICS VIA A SELF-SUPERVISED LEARNING FRAMEWORK
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机译：用于培训深层模型的系统，方法和设备，以通过自我监督的学习框架学习嵌入部分整体语义内的对比表示
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摘要
Described herein are means for training a deep model to learn contrastive representations embedded within part-whole semantics via a self-supervised learning framework, in which the trained deep models are then utilized for the processing of medical imaging. For instance, an exemplary system is specifically configured for performing a random cropping operation to crop a 3D cube from each of a plurality of medical images received at the system as input, performing a resize operation of the cropped 3D cubes, performing an image reconstruction operation of the resized and cropped 3D cubes to predict the resized whole image represented by the original medical images received; and generating a reconstructed image which is analyzed for reconstruction loss against the original image representing a known ground truth image to the reconstruction loss function. Other related embodiments are disclosed.
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