DEEP CONVOLUTIONAL NEURAL NETWORK WITH SELF-TRANSFER LEARNING
First Claim
1. A convolutional neural network system, comprising:
- a memory that stores computer executable components;
a processor that executes computer executable components stored in the memory, wherein the computer executable components comprise;
a machine learning component that performs a first convolutional layer process associated with sequential downsampling of medical imaging data followed by a second convolutional layer process associated with sequential upsampling of the medical imaging data to facilitate generation of learned medical imaging output regarding an anatomical region, wherein a first convolutional layer of the first convolutional layer process corresponds to a last convolutional layer of the second convolutional layer process; and
a medical imaging diagnosis component that predicts a medical condition associated with the anatomical region based on the learned medical imaging output.
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Abstract
Systems and techniques for facilitating a deep convolutional neural network with self-transfer learning are presented. In one example, a system includes a machine learning component, a medical imaging diagnosis component and a visualization component. The machine learning component generates learned medical imaging output regarding an anatomical region based on a convolutional neural network that receives medical imaging data. The machine learning component also performs a plurality of sequential downsampling and upsampling of the medical imaging data associated with convolutional layers of the convolutional neural network. The medical imaging diagnosis component determines a classification and an associated localization for a portion of the anatomical region based on the learned medical imaging output associated with the convolutional neural network. The visualization component generates a multi-dimensional visualization associated with the classification and the localization for the portion of the anatomical region.
3 Citations
20 Claims
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1. A convolutional neural network system, comprising:
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a memory that stores computer executable components; a processor that executes computer executable components stored in the memory, wherein the computer executable components comprise; a machine learning component that performs a first convolutional layer process associated with sequential downsampling of medical imaging data followed by a second convolutional layer process associated with sequential upsampling of the medical imaging data to facilitate generation of learned medical imaging output regarding an anatomical region, wherein a first convolutional layer of the first convolutional layer process corresponds to a last convolutional layer of the second convolutional layer process; and a medical imaging diagnosis component that predicts a medical condition associated with the anatomical region based on the learned medical imaging output. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method, comprising:
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performing, by a system comprising a processor, iterative sequential downsampling and upsampling of medical imaging data associated with convolutional layers of a convolutional neural network to generate learned medical imaging output regarding a patient body, wherein a first convolutional layer for the downsampling corresponds to a last convolutional layer for the upsampling; and predicting, by the system, a medical condition associated with a portion of the patient body based on the learned medical imaging output. - View Dependent Claims (10, 11, 12, 13, 14, 15)
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16. A computer readable storage device comprising instructions that, in response to execution, cause a system comprising a processor to perform operations, comprising:
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performing a first convolutional layer process associated with sequential downsampling of medical imaging data; performing a second convolutional layer process associated with sequential upsampling of the medical imaging data, wherein a first convolutional layer of the first convolutional layer process corresponds to a last convolutional layer of the second convolutional layer process; and predicting a medical condition associated with an anatomical region based on learned medical imaging output associated with the first convolutional layer process and the second convolutional layer process. - View Dependent Claims (17, 18, 19, 20)
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Specification