A method, system and computer-readable storage medium for performing a cognitive information processing operation. The cognitive information processing operation includes: receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via an augmented intelligence system, the augmented intelligence system executing on a hardware processor of an information processing system, the augmented intelligence system and the information processing system providing a cognitive computing function; performing a robustness assessment operation, the robustness assessment operation assessing robustness of the cognitive computing function, the robustness assessment operation generating a robustness score representing robustness of the cognitive computing function; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.

The present disclosure provides systems and methods for predicting a disease state of a subject using ultrasound imaging. The method includes identifying at least one quantitative measurement of a subject using ultrasound imaging, the at least one quantitative measurement included as part of quantitative information of the subject gathered based on the ultrasound imaging, comparing the at least one quantitative measurement to a first predetermined standard to determine a first initial value, the first predetermined standard falling within a first range of quantities, identifying at least one qualitative measurement of the subject using the ultrasound imaging, the at least one qualitative measurement included as part of qualitative information of the subject gathered based on the ultrasound imaging, comparing the at least one qualitative measurement to a second predetermined standard to determine a second initial value, the second predetermined standard falling within a second range of quantities; and correlating at least the quantitative information and the qualitative information using the first initial value and the second initial value to determine a final value that is used in predicting a disease state of the subject.

The present disclosure provides systems and methods for predicting a disease state of a subject using ultrasound imaging. The method includes identifying at least one quantitative measurement of a subject using ultrasound imaging, the at least one quantitative measurement included as part of quantitative information of the subject gathered based on the ultrasound imaging, comparing the at least one quantitative measurement to a first predetermined standard to determine a first initial value, the first predetermined standard falling within a first range of quantities, identifying at least one qualitative measurement of the subject using the ultrasound imaging, the at least one qualitative measurement included as part of qualitative information of the subject gathered based on the ultrasound imaging, comparing the at least one qualitative measurement to a second predetermined standard to determine a second initial value, the second predetermined standard falling within a second range of quantities; and correlating at least the quantitative information and the qualitative information using the first initial value and the second initial value to determine a final value that is used in predicting a disease state of the subject.

An information processing apparatus includes: a memory configured to store an image processing program having a tree structure in which a partial program is incorporated in each of a plurality of nodes; and a processor configured to performs, based on the image processing program, operations of; calculating a feature amount based on a processing result in each intermediate node excluding a terminal node among the plurality of nodes when executing image processing on a captured image which is captured by an imaging device; and calculating a performance evaluation value of the image processing program based on a variation amount of the feature amount in accordance with elapse of time.

A method comprises displaying, via an interactive interface, a medical scan and a plurality of prompts of each prompt decision tree of a plurality of prompt decision trees in succession, beginning with automatically determined starting prompts of each prompt decision tree, in accordance with corresponding nodes of each prompt decision tree until a leaf node of each prompt decision tree is ultimately selected. Labeling data indicating the ultimately selected leaf node of each prompt decision tree is determined for the medical scan.

A method, system and computer-readable storage medium for performing a counterfactual generation operation. The counterfactual generation operation includes: receiving a subject data point; classifying the data point via a trained classifier, the classifying providing a classified data point; identifying a counterfactual using the classified data point, the counterfactual comprising another datapoint, the another data point being close to the subject data point, the another data point resulting in production of a different outcome when provided to a model when compared to an outcome resulting from the subject data point being provided to the model; and, providing the counterfactual to a destination.

A method, system and computer-readable storage medium for performing a counterfactual generation operation. The counterfactual generation operation includes: receiving a subject data point; classifying the data point via a trained classifier, the classifying providing a classified data point; identifying a counterfactual using the classified data point, the counterfactual comprising another datapoint, the another data point being close to the subject data point, the another data point resulting in production of a different outcome when provided to a model when compared to an outcome resulting from the subject data point being provided to the model; and, providing the counterfactual to a destination.

The technology disclosed relates to a system for inter-model prediction score recalibration. The system includes a first model that generates, based on evolutionary conservation summary statistics of amino acids in a reference protein sequence, a first set of pathogenicity scores with rankings for variants that mutate the reference sequence to alternate protein sequences. The system further includes a second model that generates, based on epistasis expressed by amino acid patterns spanning a multiple sequence alignment aligning the reference sequence to non-target sequences, a second set of pathogenicity scores with rankings for the variants. The system further includes a rank loss determination logic that determines a rank loss parameter by comparing the two sets of rankings, a loss function reconfiguration logic that reconfigures a loss function based on the rank loss parameter, and a training logic that uses the reconfigured loss function to train the first model.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting intermediate reinforcement learning goals. One of the methods includes obtaining a plurality of demonstration sequences, each of the demonstration sequences being a sequence of images of an environment while a respective instance of a reinforcement learning task is being performed; for each demonstration sequence, processing each image in the demonstration sequence through an image processing neural network to determine feature values for a respective set of features for the image; determining, from the demonstration sequences, a partitioning of the reinforcement learning task into a plurality of subtasks, wherein each image in each demonstration sequence is assigned to a respective subtask of the plurality of subtasks; and determining, from the feature values for the images in the demonstration sequences, a respective set of discriminative features for each of the plurality of subtasks.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting intermediate reinforcement learning goals. One of the methods includes obtaining a plurality of demonstration sequences, each of the demonstration sequences being a sequence of images of an environment while a respective instance of a reinforcement learning task is being performed; for each demonstration sequence, processing each image in the demonstration sequence through an image processing neural network to determine feature values for a respective set of features for the image; determining, from the demonstration sequences, a partitioning of the reinforcement learning task into a plurality of subtasks, wherein each image in each demonstration sequence is assigned to a respective subtask of the plurality of subtasks; and determining, from the feature values for the images in the demonstration sequences, a respective set of discriminative features for each of the plurality of subtasks.