Techniques for training a deep neural network from user interaction workflow activities occurring among distributed computing devices are disclosed herein. In an example, processing of input data (such as input medical imaging data) is performed at a client computing device with the execution of an algorithm of a deep neural network. A set of updated training parameters are generated to update the algorithm of the deep neural network, based on user interaction activities (such as user acceptance and user modification in a graphical user interface) that occur with the results of the executed algorithm. The generation and collection of the updated training parameters at a server, received from a plurality of distributed client sites, can be used to refine, improve, and train the algorithm of the deep neural network for subsequent processing and execution.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for training a deep neural network from workflow activities in a computing device, performed by electronic operations executed by the computing device, with the computing device having at least one processor and at least one memory, and with the electronic operations comprising: generating model output of source data in a graphical user interface of the computing device, wherein the model output of the source data is produced using execution of an algorithm of a deep neural network on a set of source data; receiving, in the graphical user interface, user acceptance of the model output of the source data generated in the graphical user interface; generating updated parameters to update the algorithm of the deep neural network, wherein the updated parameters to update the algorithm are based on the user acceptance that is received in the graphical user interface; and transmitting, to a parameter server, the updated parameters to update the algorithm of the deep neural network. 2. The method of claim 1, wherein the updated parameters to update the algorithm of the deep neural network provide reinforcement of weights used by the algorithm, in response to user input received with the computing device, and wherein the user input indicates the user acceptance that is received in the graphical user interface. 3. The method of claim 1, the electronic operations comprising: receiving, in the graphical user interface, user modification of the model output of the source data generated in the graphical user interface; wherein the updated parameters to update the algorithm of the deep neural network provide changes of weights used by the algorithm, in response to user input received with the computing device; and wherein the user input indicates the user modification that is received in the graphical user interface. 4. The method of claim 3, the electronic operations comprising: calculating a difference between the model output of the source data and updated output of the source data, wherein the updated output of the source data is provided from the user modification of the model output; wherein the updated parameters to update the algorithm of the deep neural network provide an indication of the calculated difference between the model output of the source data and the updated output of the source data. 5. The method of claim 4, wherein calculating the difference between the model output of the source data and updated output includes calculating changes to a plurality of weights applied by the algorithm of the deep neural network, and wherein the updated parameters to update the algorithm of the deep neural network indicate the changes to the plurality of weights. 6. The method of claim 1, the electronic operations comprising: executing a user interaction workflow, the user interaction workflow including the operations of generating the model output of the source data, the user interaction workflow performed with an execution of a first version of the algorithm of the deep neural network; executing a parallel algorithm workflow concurrently with the user interaction workflow, the parallel algorithm workflow including the operations of generating an expected model output of the source data, wherein the expected model output of the source data is produced using an execution of a second version of the algorithm of the deep neural network, wherein the second version of the algorithm of the deep neural network operates with received parameters provided from the parameter server; receiving, in the graphical user interface, user modifications of the model output of the source data generated in the graphical user interface, prior to receiving the user acceptance; and determining a difference in parameters used in the first version of the algorithm of the deep neural network and the parameters used in the second version of the algorithm of the deep neural network; wherein transmitting the updated parameters for training of the deep neural network includes transmitting the determined difference in parameters. 7. The method of claim 1, wherein the source data is medical imaging data that represents one or more human anatomical features in one or more medical images, wherein the algorithm of the deep neural network performs automated workflow operations, including at least one of: detection, segmentation, quantification, or prediction operations, and wherein the automated workflow operations are performed on identified characteristics of one or more of the human anatomical features in the one or more medical images. 8. The method of claim 7, wherein the model output of the source data includes a change in visualization to a display of the one or more human anatomical features in the one or more medical images, and wherein the change in visualization to the display of the one or more human anatomical features in the one or more medical images is further changed by a user modification received with the computing device, wherein the user modification received with the computing device causes a further change to the visualization to the display of the one or more of the human anatomical features, the user modification received from a first user input received with the computing device via a human input device, and wherein the user acceptance received with the computing device causes an acceptance of the further change to the visualization of the display of the one or more of the human anatomical features, the user acceptance received from a second user input received with the computing device via the human input device. 9. The method of claim 1, the electronic operations comprising: receiving, from the parameter server, subsequent received parameters for subsequent operation of the algorithm for the deep neural network; and operating the algorithm for the deep neural network on a subsequent set of source data, based on use of the subsequent received parameters with the algorithm of the deep neural network. 10. At least non-transitory machine-readable medium, the machine-readable medium including instructions, which when executed by a machine having a hardware processor, causes the machine to perform operations that: generate model output of source data in a graphical user interface, wherein the model output of the source data is produced using execution of an algorithm of a deep neural network on a set of source data; receive, in the graphical user interface, user acceptance of the model output of the source data generated in the graphical user interface; generate updated parameters to update the algorithm of the deep neural network, wherein the updated parameters to update the algorithm are based on the user acceptance that is received in the graphical user interface; and transmit, to a parameter server, the updated parameters to update the algorithm of the deep neural network. 11. The machine-readable medium of claim 10, wherein the updated parameters to update the algorithm of the deep neural network provide reinforcement of weights used by the algorithm, in response to received user input, and wherein the received user input indicates the user acceptance that is received in the graphical user interface. 12. The machine-readable medium of claim 10, the medium further including instructions that cause the machine to perform operations that: receive, in the graphical user interface, user modification of the model output of the source data generated in the graphical user interface; wherein the updated parameters to update the algorithm of the deep neural network provide changes of weights used by the algorithm, in response to received user input; and wherein the received user input indicates the user modification that is received in the graphical user interface. 13. The machine-readable medium of claim 12, the medium further including instructions that cause the machine to perform operations that: calculate a difference between the model output of the source data and updated output of the source data, wherein the updated output of the source data is provided from the user modification of the model output; wherein the updated parameters to update the algorithm of the deep neural network provide an indication of the calculated difference between the model output of the source data and the updated output of the source data. 14. The machine-readable medium of claim 13, wherein calculating the difference between the model output of the source data and updated output includes calculating changes to a plurality of weights applied by the algorithm of the deep neural network, and wherein the updated parameters to update the algorithm of the deep neural network indicate the changes to the plurality of weights. 15. The machine-readable medium of claim 10, the medium including instructions that cause the machine to perform operations that: execute a user interaction workflow, the user interaction workflow including the operations of generating the model output of the source data, the user interaction workflow performed with an execution of a first version of the algorithm of the deep neural network; execute a parallel algorithm workflow concurrently with the user interaction workflow, the parallel algorithm workflow including the operations of generating an expected model output of the source data, wherein the expected model output of the source data is produced using an execution of a second version of the algorithm of the deep neural network, wherein the second version of the algorithm of the deep neural network operates with received parameters provided from the parameter server; receive, in the graphical user interface, user modifications of the model output of the source data generated in the graphical user interface, prior to receiving the user acceptance; and determine a difference in parameters used in the first version of the algorithm of the deep neural network and the parameters used in the second version of the algorithm of the deep neural network; wherein transmitting the updated parameters for training of the deep neural network includes transmitting the determined difference in parameters. 16. The machine-readable medium of claim 10, wherein the source data is medical imaging data that represents one or more human anatomical features in one or more medical images, and wherein the algorithm of the deep neural network performs automated workflow operations, including at least one of: detection, segmentation, quantification, or prediction operations, and wherein the automated workflow operations are performed on identified characteristics of one or more of the human anatomical features in the one or more medical images. 17. The machine-readable medium of claim 16, wherein the model output of the source data includes a change in visualization to a display of the one or more human anatomical features in the one or more medical images, and wherein the change in visualization to the display of the one or more human anatomical features in the one or more medical images is further changed by user modification, wherein the user modification causes a further change to the visualization to the display of the one or more of the human anatomical features, the user modification received from a first user input received via a human input device, and wherein the user acceptance causes an acceptance of the further change to the visualization of the display of the one or more of the human anatomical features, the user acceptance received from a second user input received via the human input device. 18. The machine-readable medium of claim 10, the medium including instructions that cause the machine to perform operations that: receive, from the parameter server, subsequent received parameters for subsequent operation of the algorithm for the deep neural network; and operate the algorithm for the deep neural network on a subsequent set of source data, based on use of the subsequent received parameters with the algorithm of the deep neural network. 19. A system, comprising: a medical imaging viewing system, comprising processing circuitry having at least one processor and at least one memory, the processing circuitry to execute instructions with the at least one processor and the at least one memory to: generate model output of source data in a graphical user interface, wherein the model output of the source data is produced using execution of an algorithm of a deep neural network on a set of source data; receive, in the graphical user interface, user acceptance of the model output of the source data generated in the graphical user interface; generate updated parameters to update the algorithm of the deep neural network, wherein the updated parameters to update the algorithm are based on the user acceptance that is received in the graphical user interface; and transmit, to a parameter server, the updated parameters to update the algorithm of the deep neural network. 20. The system of claim 19, the processing circuitry to execute further instructions with the at least one processor and the at least one memory to: calculate the updated parameters to update the algorithm of the deep neural network to provide reinforcement of weights used by the algorithm, in response to received user input; wherein the user input indicates the user acceptance that is received in the graphical user interface; wherein the source data is medical imaging data that represents human anatomical features in one or more medical images; wherein the algorithm of the deep neural network performs automated workflow operations, including at least one of: detection, segmentation, quantification, or prediction operations; and wherein the automated workflow operations are performed on identified characteristics of one or more of the human anatomical features in the one or more medical images. 21. The system of claim 19, the processing circuitry to execute further instructions with the at least one processor and the at least one memory to: receive, in the graphical user interface, user modification of the model output of the source data generated in the graphical user interface; and calculate a difference between the model output of the source data and updated output of the source data, wherein the updated output of the source data is provided from the user modification of the model output; wherein the updated parameters to update the algorithm of the deep neural network provide changes of weights used by the algorithm, in response to received user input; wherein the updated parameters to update the algorithm of the deep neural network provide an indication of the calculated difference between the model output of the source data and the updated output of the source data; and wherein the user input indicates the user modification that is received in the graphical user interface. 22. The system of claim 19, the processing circuitry to execute further instructions with the at least one processor and the at least one memory to: execute a user interaction workflow, the user interaction workflow including operations to generate the model output of the source data, the user interaction workflow performed with an execution of a first version of the algorithm of the deep neural network; execute a parallel algorithm workflow concurrently with the user interaction workflow, the parallel algorithm workflow including the operations of generating an expected model output of the source data, wherein the expected model output of the source data is produced using an execution of a second version of the algorithm of the deep neural network, wherein the second version of the algorithm of the deep neural network operates with received parameters provided from the parameter server; receive, in the graphical user interface, user modifications of the model output of the source data generated in the graphical user interface, prior to receiving the user acceptance; and determine a difference in parameters used in the first version of the algorithm of the deep neural network and the parameters used in the second version of the algorithm of the deep neural network; wherein transmission of the updated parameters for training of the deep neural network includes transmission of the determined difference in parameters.