Unsupervised Deep Representation Learning for Fine-grained Body Part Recognition
First Claim
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; 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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Abstract
A method and apparatus for deep learning based fine-grained body part recognition in medical imaging data is disclosed. A paired convolutional neural network (P-CNN) for slice ordering is trained based on unlabeled training medical image volumes. A convolutional neural network (CNN) for fine-grained body part recognition is trained by fine-tuning learned weights of the trained P-CNN for slice ordering. The CNN for fine-grained body part recognition is trained to calculate, for an input transversal slice of a medical imaging volume, a normalized height score indicating a normalized height of the input transversal slice in the human body.
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Citations
22 Claims
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1. A method for deep learning based fine-grained body part recognition in medical imaging data, comprising:
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training a paired convolutional neural network (P-CNN) for slice ordering based on unlabeled training medical image volumes; 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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. An apparatus for deep learning based fine-grained body part recognition in medical imaging data, comprising:
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means for training a paired convolutional neural network (P-CNN) for slice ordering based on unlabeled training medical image volumes; and means for 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. - View Dependent Claims (10, 11, 12, 13, 14)
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15. The apparatus of 14, wherein the means for calculating normalized height scores for transversal slices of the annotated medical imaging volumes based on the normalized body height model comprises:
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means for assigning a respective normalized height value for the location of each of the set of anatomical landmarks in each of the annotated medical imaging volumes as the average normalized height value of that anatomical landmark in the normalized body height model; and means for interpolating between the anatomical landmarks in the set of anatomical landmarks to determine normalized height values for the transversal slices between the anatomical landmark locations in each of the annotated medical imaging volumes.
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16. A non-transitory computer readable medium storing computer program instructions for deep learning based fine-grained body part recognition in medical imaging data, the computer program instructions when executed on a processor cause the processor to perform operations comprising:
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training a paired convolutional neural network (P-CNN) for slice ordering based on unlabeled training medical image volumes; 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. - View Dependent Claims (17, 18, 19, 20, 21, 22)
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Specification