A computer system obtains a plurality of road images captured by one or more cameras attached to one or more vehicles. The one or more vehicles execute a model that facilitates driving of the one or more vehicles. For each road image of the plurality of road images, the computer system determines, in the road image, a fraction of pixels having an ambiguous lane marker classification. Based on the fraction of pixels, the computer system determines whether the road image is an ambiguous image for lane marker classification. In accordance with a determination that the road image is an ambiguous image for lane marker classification, the computer system enables labeling of the image and adds the labeled image into a corpus of training images for retraining the model.

A computer system obtains a plurality of road images captured by one or more cameras attached to one or more vehicles. The one or more vehicles execute a model that facilitates driving of the one or more vehicles. For each road image of the plurality of road images, the computer system determines, in the road image, a fraction of pixels having an ambiguous lane marker classification. Based on the fraction of pixels, the computer system determines whether the road image is an ambiguous image for lane marker classification. In accordance with a determination that the road image is an ambiguous image for lane marker classification, the computer system enables labeling of the image and adds the labeled image into a corpus of training images for retraining the model.

A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing hybrid machine learning. More specifically, the system and method produce predictions of MCI conversions to dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is trained using transfer learning. A platform may then receive a request from a clinician for a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.

A method includes tagging, by at least one processor, one or more three-dimensional assets with a unique identifier and storing the one or more three-dimensional assets in a database, creating, by the at least one processor, a three-dimensional model based on the one or more three-dimensional assets and loading the three-dimensional model in a simulator, generating, by the at least one processor, a two-dimensional image that is a representation of the three-dimensional model in the simulator, the two-dimensional image comprising metadata that includes each unique identifier for each three-dimensional asset of the three-dimensional model displayed in the two-dimensional image, and assigning, by the at least one processor, the two-dimensional image with a unique identifier and storing each unique identifier for each three-dimensional asset of the three-dimensional model displayed in the two-dimensional image in metadata for the two-dimensional image.

A radiation imaging control apparatus is provided that includes a camera imaging control unit configured to control a camera apparatus to image an implementation state of a radiation imaging examination, a subject body shape recognition unit configured to recognize a body shape in an imaging part of a subject by using a camera image imaged by the camera apparatus under a control of the camera imaging control unit, a specifying unit configured to specify a radiation imaging setting related to the radiation imaging examination by using the body shape in the imaging part of the subject recognized by the subject body shape recognition unit, and a selecting unit configured to select the radiation imaging setting specified by the specifying unit as setup information of the radiation imaging examination.

A user-generated graphical representation can be sent into a generative network to generate a synthetic image of an area including a road, the user-generated graphical representation including at least three different colors and each color from the at least three different colors representing a feature from a plurality of features. A determination can be made that a discrimination network fails to distinguish between the synthetic image and a sensor detected image. The synthetic image can be sent, in response to determining that the discrimination network fails to distinguish between the synthetic image and the sensor-detected image, into an object detector to generate a non-user-generated graphical representation. An objective function can be determined based on a comparison between the user-generated graphical representation and the non-user-generated graphical representation. A perception model can be trained using the synthetic image in response to determining that the objective function is within a predetermined acceptable range.

A user-generated graphical representation can be sent into a generative network to generate a synthetic image of an area including a road, the user-generated graphical representation including at least three different colors and each color from the at least three different colors representing a feature from a plurality of features. A determination can be made that a discrimination network fails to distinguish between the synthetic image and a sensor detected image. The synthetic image can be sent, in response to determining that the discrimination network fails to distinguish between the synthetic image and the sensor-detected image, into an object detector to generate a non-user-generated graphical representation. An objective function can be determined based on a comparison between the user-generated graphical representation and the non-user-generated graphical representation. A perception model can be trained using the synthetic image in response to determining that the objective function is within a predetermined acceptable range.

Described are system, method, and computer-program product embodiments for developing an object detection model. The object detection model may detect a physical object in an image of a real world environment. A system can automatically generate a plurality of synthetic images. The synthetic images can be generated by randomly selecting parameters of the environmental features, camera intrinsics, and a target object. The system may automatically annotate the synthetic images to identify the target object. In some embodiments, the annotations can include information about the target object determined at the time the synthetic images are generated. The object detection model can be trained to detect the physical object using the annotated synthetic images. The trained object detection model can be validated and tested using at least one image of a real world environment. The image(s) of the real world environment may or may not include the physical object.

Described are system, method, and computer-program product embodiments for developing an object detection model. The object detection model may detect a physical object in an image of a real world environment. A system can automatically generate a plurality of synthetic images. The synthetic images can be generated by randomly selecting parameters of the environmental features, camera intrinsics, and a target object. The system may automatically annotate the synthetic images to identify the target object. In some embodiments, the annotations can include information about the target object determined at the time the synthetic images are generated. The object detection model can be trained to detect the physical object using the annotated synthetic images. The trained object detection model can be validated and tested using at least one image of a real world environment. The image(s) of the real world environment may or may not include the physical object.

The technology described herein obfuscates image content using a local neural network and a remote neural network. The local network runs on a local computer system and a remote classifier runs in a remote computing system. Together, the local network and the remote classifier are able to classify images, while the image never leaves the local computer system. In aspects of the technology, the local network receives a local image and creates a transformed object. The transformed object may be generated by processing the image with a local neural network to generate a multidimensional array and then randomly shuffling data locations within a multidimensional array. The transformed object is communicated to the remote classifier in the remote computing system for classification. The remote classifier may not have the seed used to deterministically scramble the spatial arrangement of data within the multidimensional array.