Abstract: This application relates to an image recognition model training method, an image recognition method, apparatus, and system. The method includes: obtaining a to-be-recognized image; extracting image feature information of the to-be-recognized image; and obtaining a lesion category recognition result of the to-be-recognized image by using the image feature information of the to-be-recognized image as an input parameter of a preset image recognition model, the image recognition model being trained by using a training image sample set comprising at least one strong-label training image sample, to determine the lesion category recognition result; and the strong-label training image sample representing an image sample having strong-label information, and the strong-label information comprising at least annotation information of a lesion category and a lesion position in the strong-label training image sample.

Abstract: In this invention, a control unit in an ophthalmic image processing device acquires an ophthalmic image captured by an ophthalmic image capture device (S11). The control unit, by inputting the ophthalmic image into a mathematical model that has been trained by a machine-learning algorithm, acquires a probability distribution in which the random variables are the coordinates at which a specific site and/or a specific boundary of a tissue is present within a region of the ophthalmic image (S14). On the basis of the acquired probability distribution, the control unit detects the specific boundary and/or the specific site (S16, S24).

Abstract: Methods and systems for generating a simulated image for a specimen are provided. One system includes one or more computer subsystems and one or more components executed by the one or more computer subsystems. The one or more components include a generative adversarial network (GAN), e.g., a conditional GAN (cGAN), trained with a training set that includes portions of design data for one or more specimens designated as training inputs and corresponding images of the one or more specimens designated as training outputs. The one or more computer subsystems are configured for generating a simulated image for a specimen by inputting a portion of design data for the specimen into the GAN.

Abstract: The present disclosure proposes an apparatus for determining a bone age of teeth. The apparatus according to the present disclosure may acquire a plurality of first teeth images of a plurality of teeth corresponding to a first gender and having a first bone age, generate a plurality of pre-processed images by pre-processing the plurality of first teeth images, generate a determination filter for determining a teeth shape for the first bone age of a human body having the first gender by training the neural network model using the plurality of pre-processed images, acquire a second teeth image of teeth of a human body having a second gender and gender information indicating the second gender, and determine a second bone age of the teeth corresponding to the second teeth image based on the second teeth image and the gender information by using the determination filter.

Abstract: There is provided a system configured to trigger system configured to trigger a function, said system comprising: at least one actuator that is associated with said function; an image capturing sensor; a computing device comprising a data processor, and a computer program product comprising a machine learning model trained for detecting an intent of a person to operate said at least one actuator, wherein said computer program product when running on said data processor: receives at least one image from said image capturing sensor; analyzes said at least one image, the analyzing comprises: subjecting said at least one image to said machine learning model; detecting presence of said intent to operate said at least one actuator in said at least one image, and triggers said function upon detecting said intent.

Abstract: A technique for selectively configuring a case of a handheld device to shield an antenna from receiving or transmitting wireless signals is disclosed. The technique includes moving a blocking element on the case between a first position and a second position. The blocking element is a physical structure that is rotatable, slidable, or removable to switch between the first position and the second position. In response to moving the blocking element to the first position, the blocking element blocks wireless signals received or transmitted by the antenna of the handheld device. In response to moving the blocking element to the second position, wireless signals can be received or transmitted through the case by the antenna.

Abstract: An abnormality detection apparatus is provided. The abnormality detection apparatus includes a first generation part configured to generate pseudo-abnormal image data by synthesizing a substantially circular image at a random position of an image of normal image data obtained by photographing equipment that includes a liquid supply and supplies a liquid from the liquid supply without an abnormality, a second generation part configured to generate a determination model for determining whether the equipment is normal or abnormal by performing learning of the normal image data and the pseudo-abnormal image data, an acquisition part configured to acquire image data obtained by photographing the equipment, and a detection part configured detect an abnormality in the equipment from the image data acquired by the acquisition part using the determination model.

Abstract: A power control method includes obtaining, by a first terminal, parameter information required for power control, where the parameter information includes at least one of a first parameter, a second parameter, and a third parameter, and determining, by the first terminal based on the parameter information, uplink transmit power used when uplink transmission is performed on a target beam or a target beam pair; where the first parameter includes a beam reception gain of a network device and/or a beam sending gain of the first terminal, where the second parameter is used to indicate interference caused by a second terminal to the first terminal on the target beam, and where the third parameter includes beam-specific target power and/or terminal-specific target power.

Abstract: A method for calculating an index of microcirculatory resistance includes determining myocardial volume by extracting myocardial images; locating a coronary artery inlet and accurately segmenting coronary arteries; generating a grid model required for calculation; determining myocardial blood flow in a rest state and CFR; calculating total flow at the coronary artery inlet in a maximum hyperemia state; determining flow in different blood vessels in a coronary artery tree in the maximum hyperemia state and then determining a flow velocity V1 in the maximum hyperemia state; obtaining the average conduction time in the maximum hyperemia state Tmn, and calculating a pressure drop ?P from the coronary artery inlet to a distal end of a coronary artery stenosis, and a mean intracoronary pressure Pd at the distal end of the stenosis Pd=Pa??P, and calculating the index of microcirculatory resistance.

Abstract: An electronic device includes a housing structure including a plurality of antennas; a hinge; a first housing connected to the hinge and including a first surface and a second surface opposite to the first surface; and a second housing connected to the hinge and including a third surface and a fourth surface opposite to the third surface, a sensor module; and a processor operatively connected to the sensor module. The processor detects an unfolding operation or a folding operation between the first housing and the second housing using the sensor module, generate folding information on the unfolding operation or the folding operation based on a folding angle formed between the first housing and the second housing, when the unfolding operation or the folding operation is detected, and control an output of at least one antenna based on the folding information.

Abstract: A method recognizes objects in an environment of a vehicle including a sensor system having at least one sensor unit for registering the environment and an evaluation unit for evaluating sensor data provided by the at least one sensor unit. The method includes receiving the sensor data in the evaluation unit, the sensor data including a plurality of chronologically successive measurements; inputting the sensor data into a machine learning module; and outputting an object state of at least one object estimated based on the sensor data by way of the machine learning module. The method further includes determining a plurality of chronologically successive future object states based on the estimated object state; ascertaining deviations between the future object states and measurements chronologically corresponding to the future object states based on the sensor data; and correcting a machine learning algorithm of the module based on the deviations.

Abstract: A deep learning-based digital staining method and system are disclosed that enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples based on autofluorescence images acquired using a fluorescent microscope. The system and method have particular applicability for the creation of digitally/virtually-stained whole slide images (WSIs) of unlabeled/unstained tissue samples that are analyzes by a histopathologist. The methods bypass the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses, in one embodiment, a convolutional neural network trained using a generative adversarial network model to transform fluorescence images of an unlabeled sample into an image that is equivalent to the brightfield image of the chemically stained-version of the same sample.

Abstract: Improved techniques for identifying objects, such as toy objects, are presented herein. In particular, a play system (e.g., game, doll playset, track set, etc.) includes a probed surface for use in obtaining electrical tomography measurements when toy objects are placed on the probed surface. The play system is configured to analyze the electrical tomography measurements to determine an identity of the toy object placed on the probed surface.

Abstract: Systems and methods are provided for analyzing, by a computing device, location data associated with a location of the computing device to determine that an image or video captured using a messaging application on the computing device is captured near a food-related venue or event, receiving input related to food associated with the food-related venue or event, sending the image or video and the input related to food associated with the food-related venue or event to a computing system to train a machine learning model for food detection, and updating the messaging application to comprise the trained machine learning model for food detection.

Abstract: An electronic device includes: a housing including a first surface facing a first direction; and a second surface facing a second direction opposite to the first direction, wherein an inner space is formed between the first surface and the second surface; a board disposed in the inner space; a geomagnetic sensor disposed on the board; and a processor. The geomagnetic sensor is configured to detect a first magnetic force, when a first cover part of the cover, including a first magnetic member, is disposed to cover the first surface from the first direction of the electronic device, and detect a second magnetic force, when a second cover part of the cover, including a second magnetic member, is disposed to cover the second surface from the second direction of the electronic device in a state where the first cover part is disposed to cover the first surface from the first direction of the electronic device.

Abstract: The present invention relates to a protective case for foldable mobile devices. The folding case according to the present invention comprises a first case and a second case, wherein the folding case further comprises a side cover which rotates by an elastic part as the foldable mobile device is folded or unfolded, and provides a full protection for the entire foldable mobile device including each corner of its hinge part. The folding case according to the present invention covers the hinge part smoothly without any unfavorable space or gap, and it provides the satisfaction with the use and the beauty of the overall appearance as the users would conceive that the two separated case parts and the hinge part operates as if those parts form one united case.

Abstract: A wireless communication system comprises: a setting notifying means for notifying, to a radio terminal, setting information related to at least one of the radio terminal's collection of measurement information and the radio terminal's reporting of the measurement information to a radio network; a status notifying means for notifying, to the radio network, a status related to at least one of the radio terminal's collection of the measurement information and the radio terminal's reporting of the measurement information; and a re-establishing means for receiving the status and determining whether to re-establish the setting information.

Abstract: A method and a system for measuring morphological parameters of an intracranial aneurysm image, the method comprises: segmenting an intracranial parent artery image from three-dimensional DICOM data of DSA (S101); segmenting the intracranial aneurysm image on the intracranial aneurysm image (S102); and measuring morphological parameters of the intracranial aneurysm image (S103). The method and the system for measuring the morphological parameters of the intracranial aneurysm image as disclosed may implement automated measurement of the intracranial aneurysm image, quickly measure morphological parameters of the intracranial aneurysm image, and guarantee consistency between measurements of morphological parameters of the aneurysm image.

Abstract: In some implementations, a target object identification system may train a machine learning model to identify a target object in a volume of solid waste. The target object identification system may receive, from at least one imaging device positioned in a waste processing facility, imaging data associated with a portion of a volume of solid waste. The target object identification system may identify, using the trained machine learning model and based at least in part on the imaging data, the target object, wherein the target object is disposed within the volume of solid waste. The target object identification system may provide, to a separator control assembly that controls a separator, an output associated with the target object, wherein the output is to facilitate removal of the target object from the volume of solid waste.

Abstract: A method for creating training data for an artificial intelligence system to classify hyperspectral data. The method including receiving a hyperspectral image from a data source, wherein the hyperspectral image includes a first pixel group associated with a first classification, forming from the hyperspectral image a first augmented image using a first augmentation algorithm and a second augmented image using a second augmentation algorithm, selecting a first group of sample pixels from the hyperspectral image, the first augmented image and the second augmented image, wherein each pixel of the selected first group of sample pixels is having an association with the first classification or with a second classification and providing the selected first group of sample pixels and the associated classifications of each pixel for an artificial training system to be used as a training data.