Abstract: Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Abstract: The present invention generally relates to an apparatus and a method for obtaining a registration error map representing a level of sharpness of an image. Many methods are known which allow determining the position of a camera with respect to an object, based on the knowledge of a 3D model of the object and the intrinsic parameters of the camera. However, regardless of the visual servoing technique used, there is no control in the image space and the object may get out of the camera field of view during servoing. It is proposed to obtain a registration error map relating to an image of the object of interest generated by computing an intersection of a re-focusing surface obtained from a 3D model of said object of interest and a focal stack based on acquired four-dimensional light-field data relating to said object of interest.

Abstract: Systems, methods and computer program products for detecting objects using a multi-detector are disclosed, according to various embodiments. In one aspect, a computer-implemented method includes defining an analysis profile comprising an initial number of analysis cycles dedicated to each of a plurality of detectors, where each detector is independently configured to detect objects according to a unique set of analysis parameters and/or a unique detector algorithm. The method also includes: receiving digital video data that depicts at least one object; analyzing the digital video data using some or all of the detectors in accordance with the analysis profile, where the analyzing produces an analysis result for each detector used in the analysis. Further, the method includes updating the analysis profile by adjusting the number of analysis cycles dedicated to at least one of the detectors based on the analysis results.

Abstract: A neural network based corrector for photon counting detectors is described. A method for photon count correction includes receiving, by a trained artificial neural network (ANN), a detected photon count from a photon counting detector. The detected photon count corresponds to an attenuated energy spectrum. The attenuated energy spectrum is related to characteristics of an imaging object and is based, at least in part, on an incident energy spectrum. The method further includes correcting, by the trained ANN, the detected photon count to produce a corrected photon count. The method may include reconstructing, by image reconstruction circuitry, an image based, at least in part, on the corrected photon count.

Abstract: There is provided a device configured to transcribe an appearance of a human being, said device comprising a common housing holding an image capturing sensor, a computing device comprising a data processor, and a computer program product comprising a first machine learning model trained for detecting and labeling human beings, a second machine learning model trained for detecting appearances of human beings and a transcription module to transcribe the detected appearances of human beings to text.

Abstract: A lesion detection and classification artificial intelligence (AI) pipeline comprising a plurality of trained machine learning (ML) computer models is provided. First ML model(s) process an input volume of medical images (VOI) to determine whether VOI depicts a predetermined amount of an anatomical structure. The AI pipeline determines whether criteria, such as a predetermined amount of an anatomical structure of interest being depicted in the input volume, are satisfied by output of the first ML model(s). If so, lesion processing operations are performed including: second ML model(s) processing the VOI to detect lesions which correspond to the anatomical structure of interest; third ML model(s) performing lesion segmentation and combining of lesion contours associated with a same lesion; and fourth ML models processing the listing of lesions to classify the lesions. The AI pipeline outputs the listing of lesions and the classifications for downstream computing system processing.

Abstract: Systems and methods for tracking healing progress of multiple adjacent wounds are provided. In one embodiment, a system may include a processor configured to receive a first image of a plurality of adjacent wounds near a form of colorized surface having colored reference elements, determine colors of the plurality of wounds, correct for local illumination conditions, receive a second image of the plurality of wounds near the form of colorized surface, to determine second colors of the plurality of wounds in the second image, match each of the plurality of wounds in the second image to a wound of the plurality of wounds in the first image, and determine an indicator of the healing progress for each of the plurality of wounds based on changes between the first image and the second image.

Abstract: Training a robust machine learning model by mapping an input data set to a first feature space, applying a transformation to the first feature space, yielding a second feature space, and training a dense model using the first feature space, and the second feature space.

Abstract: Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Abstract: Disclosed herein are methods and system for training artificial intelligence models configured to execute image segmentation techniques. The methods and system describe a server that receives a first image including a set of pixels depicting multiple objects. The server also receives a second image having a second set of pixels depicting the same set of objects. The server then analyzes the pixels from the first and second images. When a difference between at least one visual attribute of a pixel within the second image and a corresponding pixel within the first image satisfies a predetermined threshold, it will be encoded as spikes to send to the model, the model will be trained using supervised STDP rule by revising weights associated with the nodes within the AI model where the node corresponds to the pixels within the first and/or the second image.

Abstract: A method for interpreting an input image by a computing device operated by at least one processor is provided. The method for interpreting an input image comprises storing an artificial intelligent (AI) model that is trained to classify a lesion detected in the input image as suspicious or non-suspicious and, under a condition of being suspicious, to classify the lesion detected in the input image as malignant or benign-hard representing that the lesion is suspicious but determined to be benign, receiving an analysis target image, by using the AI model, obtaining a classification class of a target lesion detected in the analysis target image and, when the classification class is the suspicious, obtaining at least one of a probability of being suspicious, a probability of being benign-hard, and a probability of malignant for the target lesion, and outputting an interpretation result including at least one probability obtained for the target lesion.

Abstract: Aspects of the subject disclosure may include, for example, analyzing media content to recognize an object therein, wherein the media content is provided as a video stream displayed to a user of a communication device, the media content comprising a plurality of digital frames transported to the communication device over a private network; associating product information of a product with the object; receiving a user input selecting the object; determining providers of goods or services of the product, wherein the providers of goods or services are selected during the determining based on a proximity of the providers to the user of the communication device; and providing the product information to the communication device. Other embodiments are disclosed.

Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with cardiac assessment. An apparatus as described herein may obtain electrocardiographic imaging (ECGI) information associated with a human heart and magnetic resonance imaging (MRI) information associated with the human heart, and integrate the ECGI and MRI information using a machine-learned model. Using the integrated ECGI and MRI information, the apparatus may predict target ablation sites, estimate electrophysiology (EP) measurements, and/or simulate the electrical system of the human heart.

Abstract: The present disclosure discloses an image haze removal method and apparatus, and a device. The method includes: acquiring a hazy image to be processed; and obtaining a haze-free image corresponding to the hazy image by inputting the hazy image into a pre-trained haze removal model. The present disclosure uses the residual dual attention fusion modules as basic modules of the neural network, so that each feature map can obtain pixel features while enhancing the global dependence, thus improving the image dehazing effect.

Abstract: A method of identifying potential lesions in mammographic images may include operations executed by an image processing device including receiving first image data of a first type, receiving second image data of a second type, registering the first image data and the second image data by employing a CNN using pixel level registration or object level registration, determining whether a candidate detection of a lesion exists in both the first image data and the second image data based on the registering of the first image data and the second image data, and generating display output identifying the lesion.

Abstract: Method for obtaining at least one significant feature in a series of components of the same type on the basis of data sets by non-destructive testing. The method includes examining a classified random sample of components which have a known production sequence, by a non-destructive testing. A three-dimensional data set for each component is obtained, and components of the sample are divided by good and rejected parts. Defect-free component regions from all of the components of the random sample are extracted. At least one feature which is characteristic of the type of component and production process which, over a predetermined time of component production, exhibits considerable characteristic differences between the good and rejected parts is determined. The determination can be accomplished using neural networks, machine learning approaches, or statistics from the field of data analytics. The at least one feature and its characteristic is defined as a trained classifier.

Abstract: According to one embodiment, a medical image processing apparatus includes processing circuitry. The processing circuitry acquires an input image based on reception data collected by transmitting/receiving ultrasound by using an ultrasound probe including a plurality of vibration elements driven in accordance with a delay profile, stores a plurality of trained models for generating, based on an input image, an output image in which noise is reduced according to a wavefront shape of when the ultrasound is transmitted in an input image, selects a trained model corresponding to a type of the ultrasound probe or the delay profile from the plurality of trained models, and generates an output image by inputting an input image to the selected trained model.

Abstract: A mutation detection apparatus includes a memory configured to store software for implementing a neural network and a processor configured to detect a mutation by executing the software, wherein the processor is configured to generate first genome data extracted from a target tissue and second genome data extracted from a normal tissue, extract image data by preprocessing the first genome data and the second genome data, and detect a mutation of the target tissue on the basis of the image data through the neural network trained to correct a sequencing platform-specific false positive.

Abstract: A system and method is provided for controlling physiological-noise in functional magnetic resonance imaging using raw k-space data to extract physiological noise effects. The method can identify these effects when they are separable and directly reflects the artefactual effects on fMRI data, without the need for external monitoring or recording devices and to be compensated for via rigorous statistical analysis modeling of such noise sources. The physiological fluctuations may be treated as global perturbations presented around the origin point in a k-space 2D slice. Each k-space 2D slice may be acquired at a very short repetition time with an effective sampling rate to sample cardiac and respiratory rhythms through proper reordering and phase-unwarping techniques applied to the raw k-space data.

Abstract: A face recognition system, a face recognition method, and a storage medium that can perform face matching smoothly in a short time are provided. The face recognition system includes: a face detection unit that detects a face image from an image including an authentication subject as a detected face image; a storage unit stores identification information identifying the authentication subject and a registered face image of the authentication subject in association with each other; and a face matching unit that, in response to acquisition of the identification information identifying the authentication subject, matches, against the registered face image corresponding to the acquired identification information, the detected face image detected by the face detection unit from an image captured before the acquisition.