Abstract: A system for generating simulated body parts for images may include a body part recognition convolutional neural network (CNN) to recognize a body part in an input image. The body part recognition CNN may be trained using first training data including training images including body parts contained in the input image being identified. The system may also include a body part generative adversarial network (GAN) to complete an image of the body part in the input image based on a body part identification output by the body part recognition CNN. The body part GAN may be trained using second training data including at least partial training images.

Abstract: A method, system and computer program product for determining changes in breast density. A generative adversarial network is trained to predict an appearance of a mammogram image for a patient's current examination based on mammogram images assigned labels of a first type of density classification. An appearance of a mammogram image for a patient's current examination is predicted using the generative adversarial network based on a mammogram image(s) obtained from the patient's prior examination assigned labels of the first type of density classification. A comparison is made between the predicted and actual mammogram images for the patient's current examination to determine if there is a difference between scores assigned to the predicted and actual mammogram images, and if so, whether such difference can be attributed to the subjective assessment by different physicians or changes in the standards of density classification or is due to a real change in breast density.

Abstract: A method, system and computer program product for determining changes in breast density. A generative adversarial network is trained to predict an appearance of a mammogram image for a patient's current examination based on mammogram images assigned labels of a first type of density classification. An appearance of a mammogram image for a patient's current examination is predicted using the generative adversarial network based on a mammogram image(s) obtained from the patient's prior examination assigned labels of the first type of density classification. A comparison is made between the predicted and actual mammogram images for the patient's current examination to determine if there is a difference between scores assigned to the predicted and actual mammogram images, and if so, whether such difference can be attributed to the subjective assessment by different physicians or changes in the standards of density classification or is due to a real change in breast density.

Abstract: A method, system and computer program product for determining changes in breast density. A generative adversarial network is trained to predict an appearance of a mammogram image for a patient's current examination based on mammogram images assigned labels of a first type of density classification. An appearance of a mammogram image for a patient's current examination is predicted using the generative adversarial network based on a mammogram image(s) obtained from the patient's prior examination assigned labels of the first type of density classification. A comparison is made between the predicted and actual mammogram images for the patient's current examination to determine if there is a difference between scores assigned to the predicted and actual mammogram images, and if so, whether such difference can be attributed to the subjective assessment by different physicians or changes in the standards of density classification or is due to a real change in breast density.

Abstract: A system for generating simulated body parts for images may include a body part recognition convolutional neural network (CNN) to recognize a body part in an input image. The body part recognition CNN may be trained using first training data including training images including body parts contained in the input image being identified. The system may also include a body part generative adversarial network (GAN) to complete an image of the body part in the input image based on a body part identification output by the body part recognition CNN. The body part GAN may be trained using second training data including at least partial training images.