Abstract: The present disclosure relates to a clothes treating apparatus that operates by executing an artificial intelligence (AI) algorithm and/or a machine learning algorithm in a 5G environment connected for Internet of Things, and a method for operating the clothes treating apparatus. The method for operating the clothes treating apparatus includes acquiring a clothing image by using a camera to photograph a user wearing clothes and standing in front of a mirror display placed on a front surface of the clothes treating apparatus, analyzing the clothing image, setting an operation mode of the clothes treating apparatus according to the result of analyzing the clothing image, and causing the clothes treating apparatus to operate according to the set operation mode. It is possible to improve user satisfaction by automatically setting and activating an operation mode of a clothes treating apparatus based on clothing image information.

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: A method for mapping brain function of a subject includes generating a lesion mask using a search light algorithm based on a plurality of anatomical images of the subject. The plurality of anatomical images are registered with atlas images by nonlinear atlas registration using the generated lesion mask to generate a warping map. A plurality of functional images of the subject are resampled using the warping map to generate a functional map, functional connectivity is computed using the functional map and a multi-layer perceptron.

Abstract: A method for constructing a panorama of teeth arch with elements of interest emphasized, comprising the steps of: extracting a teeth arch from a volumetric image; unfolding the extracted teeth arch into a panoramic ribbon; assigning weighted priorities to at least two points in the panoramic ribbon, wherein priorities are weighted higher for points inside or proximal to elements of interest and applying a weighted summation in a direction perpendicular to teeth arch resulting in the panorama with elements of interest emphasized.

Abstract: Lesions associated with acute ischemic stroke are automatically segmented in images acquired with computed tomography (“CT”) using a trained machine learning algorithm (e.g., a neural network). The machine learning algorithm is trained on labeled data and associated CT data (e.g., non-contrast CT data and CT angiography source image (“CTA-SI”) data). The labeled data can include segmented data indicating lesions, which are generated by segmenting diffusion-weighted magnetic resonance images acquired within a specified time window from when the associated CT data were acquired. CT data (e.g., non-contrast CT data and CTA-SI data) acquired from a subject are then acquired and input to the trained machine learning algorithm to generate output as segmented CT data, which indicate lesions in the subject.

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 modal(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 determining a 3D centerline of a vessel are provided. A current state observation of an artificial agent is determined based on one or more image view sets, each including 2D medical images of a vessel, a current position of the artificial agent in the 2D medical images, and a start position and a target position in the 2D medical images. Policy values are calculated for a plurality of actions for moving the artificial agent in 3D based on the current state observation using a trained machine learning model. The artificial agent is moved according to a particular action based on the policy values. The steps of determining, calculating, and moving are repeated for a plurality of iterations to move the artificial agent along a 3D path between the start position and the target position. The 3D centerline of the vessel is determined as the 3D path.

Abstract: A computer-implemented method of reducing noise and artifacts in medical images is provided. The method includes receiving a series of medical images along a first dimension, wherein the signals in the medical images having a higher correlation in the first dimension than the noise and the artifacts in the medical images. The method further includes, for each of a plurality of pixels in the medical images, deriving a series of data points along the first dimension based on the series of medical images, inputting the series of data points into a neural network model, and outputting the component of signals in the series of data points. The neural network model is configured to separate a component of signals from a component of noise and artifacts in the series of data points. The method further includes generating a series of corrected medical images based on the outputted component of signals.

Abstract: Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.

Abstract: Briefly, a variety of embodiments, including the following, are described: a system embodiment and methods that allow random access to voice messages, in contrast to sequential access in existing system embodiments; a system embodiment and methods that allow for the optional use of voice recognition to enhance usability; and a system embodiment and methods that apply to the area of voicemail.

Abstract: Methods, systems, and/or apparatuses are described for detecting relevant motion of objects of interest (e.g., persons and vehicles) in surveillance videos. As described herein input data based on a plurality of captured images and/or video is received. The input data may then be pre-processed and used as an input into a convolution network that may, in some instances, have elements that perform both spatial-wise max pooling and temporal-wise max pooling. Based on The convolution network may be used to generate a plurality of prediction results of relevant motion of the objects of interest.

Abstract: A novel GAN is trained to predict high fidelity synthetic images based on low quality input dental images. The GAN further takes input anatomic masks as inputs with each image, the masks labeling pixels of the image corresponding to dental features. The GAN includes an encoder-decoder generator with semantically aware normalization between stages of the decoder according to the masks. The predicted synthetic dental image and an unpaired dental image are evaluated by a first discriminator of the GAN to obtain a realism estimate. The synthetic image and an unpaired dental image may be processed using a pretrained dental encoder to obtain a perceptual loss. The GAN is trained with the realism estimate, perceptual loss, and L1 loss. Utilization may include inputting noisy, low contrast, low resolution, blurry, or degraded dental images and outputting high resolution, denoised, high contrast, deobfuscated, and sharp dental images.

Abstract: Briefly, a variety of embodiments, including the following, are described: a system embodiment and methods that allow random access to voice messages, in contrast to sequential access in existing system embodiments; a system embodiment and methods that allow for the optional use of voice recognition to enhance usability; and a system embodiment and methods that apply to the area of voicemail.

Abstract: Embodiments disclosed herein are directed to an autonomous camera-to-camera scene change detection system whereby a first camera controls a second camera without human input. More specifically, a first camera having a field of view may receive and process an image. Based on the processed image, the first camera sends instructions to a second camera to focus in on an area of interest or a target identified in the processed image.

Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with imagery data processing. The neural networks may be pre-trained to learn parameters or models for processing the imagery data and upon deployment the neural networks may automatically perform further optimization of the learned parameters or models based on a small set of online data samples. The online optimization may be facilitated via offline meta-learning so that the optimization may be accomplished quickly in a few optimization steps.

Abstract: Medical image data is received at a data processing system, which is an artificial intelligence-based system. An identification process is performed at the data processing system to identify a subset of the medical image data representing a region of interest including one or more target tendons. A determination process is performed at the data processing system to determine one or more characteristics relating to one or more abnormalities of the one or more target tendons. Abnormality data is output, the abnormality data relating to the one or more abnormalities and being based on the one or more characteristics.

Abstract: A controller for qualifying image registration includes a memory that stores instructions; and a processor that executes the instructions. When executed by the processor, the instructions cause the controller to execute a process that includes receiving first imagery of a first modality and receiving second imagery of a second modality. The process executed by the controller also includes registering the first imagery of the first modality to the second imagery of the second modality to obtain an image registration. The image registration is subjected to an automated analysis as a qualifying process to qualify the image registration. The image registration is variably qualified when the image registration passes the qualifying process, and is not qualified when the image registration does not pass the qualifying process.

Abstract: The present application discloses an image processing method, an apparatus, an electronic device and a storage medium. A specific implementation is: acquiring an image to be processed; acquiring a grading array according to the image to be processed and a grading network model, where the grading network model is a model pre-trained according to mixed samples, the number of elements contained in the grading array is C?1, C is the number of lesion grades, C lesion grades include one lesion grade without lesion and C?1 lesion grades with lesion, and a kth element in the grading array is a probability of a lesion grade corresponding to the image to be processed being greater than or equal to a kth lesion grade, where 1?k?C?1, and k is an integer; determining the lesion grade corresponding to the image to be processed according to the grading array.

Abstract: The present disclosure relates to a computer implemented medical analysis method for predicting metastases (300) in a test tissue sample, the method comprising: providing a first machine learning model (154) having an input and an output, receiving a description (401) of a tumor (304) and first image data (148) of a test tissue sample of an anatomy region (306), the test tissue sample being free of metastases (300), providing the first image data (148) and the tumor description (401) to the input of the first machine learning model (154), in response to the providing, receiving from the output of the first machine learning model (154) a prediction of occurrence of metastases (300) originating from the tumor (304) in the test tissue sample, and providing the prediction.

Abstract: A GAN is trained to process input images and produce a synthetic dental image. The GAN further takes masks as inputs with each image, the masks labeling pixels of the image corresponding to dental features (anatomy and/or treatments). The GAN includes an encoder-decoder with normalization between stages of the decoder according to the masks. A synthetic image and an unpaired dental image is evaluated by a first discriminator of the GAN to obtain a realism estimate. The synthetic image and an unpaired dental image may be processed using a pretrained dental encoder to obtain a perceptual loss. The GAN is trained with the realism estimate and perceptual loss. Utilization may include modifying a mask for an input image to include or exclude a shape of a feature such that the synthetic image includes or excludes a dental feature.