An automatic target recognizer system including: a database that stores target recognition data including multiple reference features associated with each of multiple reference targets; a pre-selector that selects a portion of the target recognition data based on a reference gating feature of the multiple reference features; a preprocessor that processes an image received from an image acquisition system which is associated with an acquired target and determines an acquired gating feature of the acquired target; a feature extractor and processor that discriminates the acquired gating feature with the reference gating feature and, if there is a match, extracts multiple segments of the image and detects the presence, absence, probability or likelihood of one of multiple features of each of the multiple reference targets; a classifier that generates a classification decision report based on a determined classification of the acquired target; and a user interface that displays the classification decision report.

The medical image recognition method includes the following steps: transmitting an accession number to a recognition module through a prediction unit; receiving an accession number and a human body image by a recognition model, and importing the human body image into a set of neural network models respectively; wherein each of the neural network models outputs at least one recognition result; the recognition module returns the recognition results to the prediction unit, and then the recognition results are stored in database.

A method for categorizing biological structure of interest (BSOI) in digitized images of biological tissues comprises a stage of identifying BSOIs in digitized images and further comprises presenting an image from the plurality of images that comprises at least one BSOI with high level of entropy to a user, receiving from the user input indicative of a category to be associated with the BSOI that had the high level of entropy and updating the cell categories classifier according to the category of the BSOI provided by the user.

Systems and methods for controlling the operation of an autonomous vehicle are disclosed herein. One embodiment performs traffic light detection at an intersection using a sensor-based traffic light detector to produce a sensor-based detection output, the sensor-based detection output having an associated first confidence level; performs traffic light detection at the intersection using a vehicle-to-infrastructure-based (V2I-based) traffic light detector to produce a V2I-based detection output, the V2I-based detection output having an associated second confidence level; performs one of (1) selecting as a final traffic-light-detection output whichever of the sensor-based detection output and the V2I-based detection output has a higher associated confidence level and (2) generating the final traffic-light-detection output by fusing the sensor-based detection output and the V2I-based detection output using a first learning-based classifier; and controls the operation of the autonomous vehicle based, at least in part, on the final traffic-light-detection output.

A medical data processing technique for determining an indication of a medical condition is disclosed. A method implementation of the technique comprises selecting (202), based on at least one property associated with medical data of a test instance, at least one model out of a plurality of models, wherein each of the plurality of models is generated by a learning algorithm and configured to provide a model-specific indication of the medical condition, determining (204), using each of the at least one selected model, a respective model-specific indication, and determining (206), based on the model-specific indications, the indication of the medical condition.

A computer-implemented method of detecting logos in a graphical rendering may comprise detecting, using a first and a second trained object detector, logos in the graphical rendering and outputting a first and a second list of detections and filtering, using at least a first and a second prior performance-based filter, the received first and second lists of detections into a first group of kept detections, a second group of discarded detections and a third group of detections. Detections in the third group of detections may be clustered in at least one cluster comprising detections that are of a same class and that are generally co-located within the electronic image. A cluster score may then be assigned to each cluster. A set of detections of logos in the graphical rendering may then be output, the set comprising the detections in the first group and a detection from each of the clusters whose assigned cluster score is greater than a respective threshold.

A system for providing remote healthcare includes receiving a video captured on a mobile device. An image quality assessor analyzes the video to provide a video quality score to the PCP in real time while the PCP is still meeting with the patient. The image quality assessor applies an ensemble neural net to identify anatomical features in the captured video.

Methods and systems for determining information for a specimen are provided. One system includes a computer subsystem and one or more components executed by the computer subsystem that include multiple deep learning (DL) models configured for determining information for a specimen based on output generated by the specimen with learning mode(s) of an imaging subsystem. The one or more components also include a knowledge distillation component configured for combining output generated by the multiple DL models. In addition, the one or more components include a final knowledge distilled DL model configured for determining information for the specimen or an additional specimen based on output generated for the specimen or the additional specimen with runtime mode(s) of the imaging subsystem. Before the final KD DL model determines the information, the knowledge distillation component is configured for supervised training of the final knowledge distilled DL model using the combined output.

A system and a method to assess abnormality are disclosed. The system is connected to an image capturing device and has multiple classification models and a processing module. Each one of the classification models is alternately trained by supervised learning and unsupervised learning. Parameters of the classification models are not identical. The processing module is connected to the classification models. The processing module receives a test image and outputs the test image to the classification models to respectively obtain multiple feature vectors of test images and to generate an abnormality assessment information.

An ophthalmologic apparatus of an embodiment example includes a front image acquiring device, a first search processor, and a second search processor. The front image acquiring device is configured to acquire a front image of a fundus of a subject's eye. The first search processor is configured to search for an interested region corresponding to an interested site of the fundus based on a brightness variation in the front image. The second search processor is configured to search for the interested region by template matching between the front image and a template image in the event that the interested region has not been detected by the first search processor.