Decision support system for medical therapy planning
AssigneeSiemens Healthineers AG
InventorsAli Kamen, Bin Lou
Life of this patent
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Abstract
For decision support in a medical therapy, machine learning provides a machine-learned generator for generating a prediction of outcome for therapy personalized to a patient. Deep learning may result in features more predictive of outcome than handcrafted features. More comprehensive learning may be provided by using multi-task learning where one of the tasks (e.g., segmentation, non-image data, and/or feature extraction) is unsupervised and/or draws on a greater number of training samples than available for outcome prediction alone.
Claims18
A method for decision support in a medical therapy system, the method comprising:
- acquiring a medical scan of a patient;
- generating a prediction of outcome of therapy for the patient from scan data of the medical scan, the outcome generated by a machine-learned multi-task generator having been trained with unsupervised learning from training data unlabeled for the outcome and having been trained from labeled training data for the outcome;
- and
- displaying an image of the outcome.
A medical imaging system for therapy decision support, the medical imaging system comprising:
- a medical imager configured to scan a patient;
- an image processor configured to predict a result of therapy for the patient in response to input of scan data from the scan to a machine-trained network, the machine-trained network including an encoder of an autoencoder trained in an unsupervised manner and a fully-connected network configured to receive an output of the encoder to predict the result;
- and
- a display configured to display the predicted result.
A method for decision support in a medical therapy system, the method comprising:
- acquiring a medical scan of a patient;
- generating values for bottleneck features of a machine-learned autoencoder having been trained with unsupervised learning from training data unlabeled for a therapy outcome;
- clustering the values for the bottleneck features;
- identifying another patient based on the values as clustered;
- and
- displaying therapy data for the other patient.
Dependencies are inferred from the claim text.