Abstract: In some examples, a system for implementing a protocol for data transmission can include a processor to transmit a public key or an encrypted name to a ledger of a source computing device and receive a scoring code, training data, and data definitions from the source computing device. The processor can also transmit predicted labels encrypted with the public key to the source computing device, the predicted labels based on the training data and the data definitions, and receive labeled test data from the source computing device. The processor can also transmit decrypted predicted labels to the source computing device, receive a list of evaluation results from a set of remote computing devices, transmit scoring results based on the list of evaluation results to the set of remote computing devices, and execute an instruction based on a techniques corresponding to a highest score from the scoring results.

Abstract: Systems and techniques are disclosed for improvement of machine learning systems based on enhanced training data. An example method includes accessing a database storing associations between objects included in medical images and classifications of the objects. A risk assessment model adapted to determine a risk condition for an object is accessed, the assessment based on features of the object. Risk conditions associated with respective objects are determined based on the risk assessment model. A group of objects associated with a first risk condition is identified. An interactive user interface is generated for display, the user interface concurrently displaying images of the group of objects. The interactive user interface enables a user to select subsets of images to be concurrently assigned a user-selected classification. User selected classifications are provided to a machine learning system adapted to update the risk assessment model based on the classifications to increase accuracy of the model.

Abstract: In some examples, a system for implementing a protocol for data transmission can include a processor to transmit a public key or an encrypted name to a ledger of a source computing device and receive a scoring code, training data, and data definitions from the source computing device. The processor can also transmit predicted labels encrypted with the public key to the source computing device, the predicted labels based on the training data and the data definitions, and receive labeled test data from the source computing device. The processor can also transmit decrypted predicted labels to the source computing device, receive a list of evaluation results from a set of remote computing devices, transmit scoring results based on the list of evaluation results to the set of remote computing devices, and execute an instruction based on a techniques corresponding to a highest score from the scoring results.

Abstract: Systems and techniques are disclosed for improvement of machine learning systems based on enhanced training data. An example method includes accessing a database storing associations between objects included in medical images and classifications of the objects. A risk assessment model adapted to determine a risk condition for an object is accessed, the assessment based on features of the object. Risk conditions associated with respective objects are determined based on the risk assessment model. A group of objects associated with a first risk condition is identified. An interactive user interface is generated for display, the user interface concurrently displaying images of the group of objects. The interactive user interface enables a user to select subsets of images to be concurrently assigned a user-selected classification. User selected classifications are provided to a machine learning system adapted to update the risk assessment model based on the classifications to increase accuracy of the model.