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Unsupervised deep representation learning for fine-grained body part recognition

  • US 10,452,899 B2
  • Filed: 08/29/2017
  • Issued: 10/22/2019
  • Est. Priority Date: 08/31/2016
  • Status: Expired due to Fees
First Claim
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1. A method for deep learning based fine-grained body part recognition in medical imaging data, comprising:

  • training a paired convolutional neural network (P-CNN) for slice ordering based on unlabeled training medical image volumes;

    wherein training the P-CNN for slice ordering based on the unlabeled training medical image volumes comprises;

    randomly sampling transversal slice pairs from the unlabeled training medical image volumes, wherein each transversal slice pair is randomly sampled from the same training volume, andtraining the P-CNN to predict a relative order of a pair of transversal slices of a medical imaging volume based on the randomly sampled transversal slice pairs, wherein the P-CNN includes two identical sub-networks for a first plurality of layers, each to extract feature from a respective slice of the pair of transversal slices, and global final layers to fuse outputs of the sub-networks and calculate a binary classification result regarding the relative order of the pair of transversal slices; and

    training a convolutional neural network (CNN) for fine-grained body part recognition by fine-tuning learned weights of the trained P-CNN for slice ordering.

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