Abstract: An embodiment for learning-based automatic selection of artificial intelligence applications. The embodiment may receive a user request for an exam, the user request including exam information. The embodiment may automatically identify an exam type cluster corresponding to the received exam information. The embodiment may automatically detect applicable AI applications corresponding to the identified exam type cluster. The embodiment may automatically run each applicable AI application on a series of relevant test sets to generate a score for each applicable AI application. The embodiment may automatically recommend to a user a highest-scoring applicable AI application.

Abstract: A mechanism is provided in a data processing system for automatic determination of b-value difference from diffusion-weighted (DW) images. The mechanism receives a series of images wherein a first image has a first b-value and a second image has an unknown b-value. The mechanism applies a generative adversarial network (GAN) model to estimate a difference between b-values in the series of images. The mechanism determines a b-value for the second image based on the first b-value and the estimated difference between b-values.

Abstract: In an approach for image registration performance assurance by optimizing system configurations, a processor evaluates alignment of a registered image and a fixed image using a pre-trained learning model. The registered image is generated with a first registration method. A processor provides a reward score to the alignment, the reward score being defined as a higher score indicating a better alignment. A processor generates a registration status represented as a feature vector that contains information about how the registered and fixed images are aligned. A processor determines a second registration method based on the reward score, the feature vector, and the first registration method.

Abstract: This invention provides a system and method for assessing risk of a breast cancer diagnosis based upon imagery of tissue and (optionally) other patient-related factors. A CAD (or similar) system analyzes the imagery and generates a plurality of numerical feature values. An assessment module receives inputs from patient factors and history and computes the risk based upon the feature values and the patient factors and history. A masking module receives inputs from the patient factors and history, and computes the risk of having a cancer, which cancer is otherwise characterized by a low probability of detection, based upon the feature values and the patient factors and history. A recall module receives inputs from the assessment module and the masking assessment module, and generates a computer-aided indication of a clinical follow-up by the patient. Results of assessment(s) can be displayed to the clinician and/or patient using a graphical interface display.

Abstract: This invention provides a system and method for assessing risk of a breast cancer diagnosis based upon imagery of tissue and (optionally) other patient-related factors. A CAD (or similar) system analyzes the imagery and generates a plurality of numerical feature values. An assessment module receives inputs from patient factors and history and computes the risk based upon the feature values and the patient factors and history. A masking module receives inputs from the patient factors and history, and computes the risk of having a cancer, which cancer is otherwise characterized by a low probability of detection, based upon the feature values and the patient factors and history. A recall module receives inputs from the assessment module and the masking assessment module, and generates a computer-aided indication of a clinical follow-up by the patient. Results of assessment(s) can be displayed to the clinician and/or patient using a graphical interface display.

Abstract: Systems and methods for utility infrastructure condition monitoring, detection, and response are disclosed. One exemplary system includes a sensor package and a monitoring and control module. The sensor package includes a plurality of sensors such as, for example, an image sensor, a video sensor, and a LiDAR sensor. The sensors may each be configured to capture data indicative of one or more conditions (e.g., an environmental condition, a structural condition, etc.) in the vicinity of the utility infrastructure. The monitoring and control includes a detection module and an alert module. The detection module is configured to receive data captured by each sensor and, based on the captured data, determine one or more conditions in the vicinity of the utility infrastructure. The detection module may be configured to then, based on the determined conditions, provide an alert for the condition using the alert module.

Abstract: This invention provides a system and method for assessing risk of a breast cancer diagnosis based upon imagery of tissue and (optionally) other patient-related factors. A CAD (or similar) system analyzes the imagery and generates a plurality of numerical feature values. An assessment module receives inputs from patient factors and history and computes the risk based upon the feature values and the patient factors and history. A masking module receives inputs from the patient factors and history, and computes the risk of having a cancer, which cancer is otherwise characterized by a low probability of detection, based upon the feature values and the patient factors and history. A recall module receives inputs from the assessment module and the masking assessment module, and generates a computer-aided indication of a clinical follow-up by the patient. Results of assessment(s) can be displayed to the clinician and/or patient using a graphical interface display.