Adversarial and dual inverse deep learning networks for medical image analysis
First Claim
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1. A method for automatically performing a medical image analysis task on a medical image of a patient, comprising:
- receiving a medical image of a patient;
inputting the medical image to a trained deep neural network; and
automatically estimating an output model that provides a result of a target medical image analysis task on the input medical image using the trained deep neural network, wherein the trained deep neural network is trained in a discriminative adversarial network based on a minimax objective function comprising
1) a first cost term related to classification, by a discriminator network of the discriminative adversarial network, of ground truth output models,
2) a second cost term related to classification, by the discriminator network, of estimated output models estimated by an estimator network of the discriminative adversarial network from input training images, and
3) a third cost term computed using a cost function that calculates an error between the ground truth output models and the estimated output models.
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Abstract
Methods and apparatus for automated medical image analysis using deep learning networks are disclosed. In a method of automatically performing a medical image analysis task on a medical image of a patient, a medical image of a patient is received. The medical image is input to a trained deep neural network. An output model that provides a result of a target medical image analysis task on the input medical image is automatically estimated using the trained deep neural network. The trained deep neural network is trained in one of a discriminative adversarial network or a deep image-to-image dual inverse network.
20 Citations
15 Claims
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1. A method for automatically performing a medical image analysis task on a medical image of a patient, comprising:
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receiving a medical image of a patient; inputting the medical image to a trained deep neural network; and automatically estimating an output model that provides a result of a target medical image analysis task on the input medical image using the trained deep neural network, wherein the trained deep neural network is trained in a discriminative adversarial network based on a minimax objective function comprising
1) a first cost term related to classification, by a discriminator network of the discriminative adversarial network, of ground truth output models,
2) a second cost term related to classification, by the discriminator network, of estimated output models estimated by an estimator network of the discriminative adversarial network from input training images, and
3) a third cost term computed using a cost function that calculates an error between the ground truth output models and the estimated output models. - View Dependent Claims (2, 3, 4, 5)
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6. An apparatus for automatically performing a medical image analysis task on a medical image of a patient, comprising:
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means for receiving a medical image of a patient; means for inputting the medical image to a trained deep neural network; and means for automatically estimating an output model that provides a result of a target medical image analysis task on the input medical image using the trained deep neural network, wherein the trained deep neural network is trained in a discriminative adversarial network based on a minimax objective function comprising
1) a first cost term related to classification, by a discriminator network of the discriminative adversarial network, of ground truth output models,
2) a second cost term related to classification, by the discriminator network, of estimated output models estimated by an estimator network of the discriminative adversarial network from input training images, and
3) a third cost term computed using a cost function that calculates an error between the ground truth output models and the estimated output models. - View Dependent Claims (7, 8, 9, 10)
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11. A non-transitory computer readable medium storing computer program instructions for automatically performing a medical image analysis task on a medical image of a patient, the computer program instructions when executed by a processor cause the processor to perform operations comprising:
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receiving a medical image of a patient; inputting the medical image to a trained deep neural network; and automatically estimating an output model that provides a result of a target medical image analysis task on the input medical image using the trained deep neural network, wherein the trained deep neural network is trained in a discriminative adversarial network based on a minimax objective function comprising
1) a first cost term related to classification, by a discriminator network of the discriminative adversarial network, of ground truth output models,
2) a second cost term related to classification, by the discriminator network, of estimated output models estimated by an estimator network of the discriminative adversarial network from input training images, and
3) a third cost term computed using a cost function that calculates an error between the ground truth output models and the estimated output models. - View Dependent Claims (12, 13, 14, 15)
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Specification