Abstract: A system, method, and computer program product for treatment planning are disclosed. The system includes at least one processing component, at least one memory component, a training module, a retrieval module, and a plan generator. The training module generates hash codes by hashing features from data sources with data source-specific hash functions, and generates superclass hash codes by hashing the generated hash codes with at least one superclass hash function. The retrieval module extracts features from case data, and locates features from the data sources that are similar to the extracted features. The plan generator calculates outcome probabilities for the case data based on known outcomes associated with the located features.

Abstract: An AI system may receive an image. The AI system may include a first AI model trained using labeled training images including images from prior mammograms to predict cancer and a second AI model trained using labeled training images including images from current mammograms to classify mammogram images. The second AI model may be initialized using the weights of the first AI model using transfer learning. The AI system may receive a classification output indicating a likely current breast cancer diagnosis or a likelihood of the user to develop breast cancer in the future.

Abstract: An approach for training, on a computer, one or more deep learning algorithms with a plurality of mammograms with known outcomes based, at least in part, on using a set of mammograms of each patient in the plurality of mammograms. The approach includes receiving a first set of mammograms of a first patient. The first set of mammograms includes an unevaluated mammogram of the first patient and a set of prior mammograms of the first patient. The approach includes the trained convolutional neural network extracting the set of features from each mammogram of the set of mammograms of the first patient. Furthermore, the approach includes using a second deep learning algorithm of the one or more deep learning algorithms to perform an evaluation of the unevaluated mammogram of the first patient based, at least in part, on the set of prior mammograms of the first patient.

Abstract: A system, method, and computer program product for treatment planning are disclosed. The system includes at least one processing component, at least one memory component, a training module, a retrieval module, and a plan generator. The training module generates hash codes by hashing features from data sources with data source-specific hash functions, and generates superclass hash codes by hashing the generated hash codes with at least one superclass hash function. The retrieval module extracts features from case data, and locates features from the data sources that are similar to the extracted features. The plan generator calculates outcome probabilities for the case data based on known outcomes associated with the located features.

Abstract: A method, computer system, and a computer program product for simulated follow-up imaging is provided. The present invention may include receiving a set of longitudinal imaging exam data associated with a patient. The received set of longitudinal imaging exam data may correspond to a series of repeated examinations of the patient conducted over time. The present invention may also include generating, using a trained learning model, a synthetic medical image associated with the patient. The generated synthetic medical image may correspond to a simulated future imaging exam of the patient predicted based on at least a portion of the series of repeated examinations of the patient conducted over time.

Abstract: A method for training an artificial intelligence (AI) system for improved health screening is provided. A processor of the AI system, where the AI system may include a combined AI model comprising one or more AI models, may receive training images. The processor may utilize, one or more AI models that each analyze the training images. The one or more AI models may include respective objective functions. The processor may receive, from the one or more AI models, the respective objective functions obtained after each of the one or more AI models are separately trained. The method my further involve submitted a combined weighted objective function to train the AI system. The combined weighted objective function may be a weighted combination of the respective objective function from each of the one or more AI models.

Abstract: An AI system may receive an image. The AI system may include a first AI model trained using labeled training images including images from prior mammograms to predict cancer and a second AI model trained using labeled training images including images from current mammograms to classify mammogram images. The second AI model may be initialized using the weights of the first AI model using transfer learning. The AI system may receive a classification output indicating a likely current breast cancer diagnosis or a likelihood of the user to develop breast cancer in the future.

Abstract: An approach for training, on a computer, one or more deep learning algorithms with a plurality of mammograms with known outcomes based, at least in part, on using a set of mammograms of each patient in the plurality of mammograms. The approach includes receiving a first set of mammograms of a first patient. The first set of mammograms includes an unevaluated mammogram of the first patient and a set of prior mammograms of the first patient. The approach includes the trained convolutional neural network extracting the set of features from each mammogram of the set of mammograms of the first patient. Furthermore, the approach includes using a second deep learning algorithm of the one or more deep learning algorithms to perform an evaluation of the unevaluated mammogram of the first patient based, at least in part, on the set of prior mammograms of the first patient.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for image artifact removal. The method, computer program product and computer system may include computing device which may receive a primary image and analyze the primary image for global artifacts and local artifacts. The computing device may, in response to identifying a global artifact in the primary image, generate a secondary image with the global artifact removed utilizing a first generative adversarial network. The computing device may, in response to identifying a local artifact in the primary image, generate a patch with the local artifact removed utilizing a second generative adversarial network. The computing device may generate a hybrid image containing a reduction of global artifacts and a reduction of local artifacts by combining the secondary image and the patch utilizing a hybrid generative adversarial network.

Abstract: A method, computer system, and a computer program product for case-adaptive image quality assessment is provided. The present invention may include detecting a current set of features in a current exam associated with a patient. The present invention may also include calculating a current set of quality measurements for the current exam based on the detected current set of features. The present invention may further include in response to determining that the calculated current set of quality measurements for the current exam is below a patient-specific image quality threshold defined by at least one prior exam associated with the patient, automatically registering a negative quality assessment for the current exam associated with the patient.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for image artifact removal. The method, computer program product and computer system may include computing device which may receive a primary image and analyze the primary image for global artifacts and local artifacts. The computing device may, in response to identifying a global artifact in the primary image, generate a secondary image with the global artifact removed utilizing a first generative adversarial network. The computing device may, in response to identifying a local artifact in the primary image, generate a patch with the local artifact removed utilizing a second generative adversarial network. The computing device may generate a hybrid image containing a reduction of global artifacts and a reduction of local artifacts by combining the secondary image and the patch utilizing a hybrid generative adversarial network.

Abstract: A method, computer system, and a computer program product for case-adaptive image quality assessment is provided. The present invention may include detecting a current set of features in a current exam associated with a patient. The present invention may also include calculating a current set of quality measurements for the current exam based on the detected current set of features. The present invention may further include in response to determining that the calculated current set of quality measurements for the current exam is below a patient-specific image quality threshold defined by at least one prior exam associated with the patient, automatically registering a negative quality assessment for the current exam associated with the patient.