Abstract: A computer-implemented method for image processing includes obtaining, based on a source image including a first biological object, an identity feature and attribute feature used to represent identity information and attribute information of the first biological object. Based on a target image including a second biological object, a context feature used to represent context information of the second biological object is obtained. Based on the identity feature, a fused image of the source image and the target image, the attribute feature, and the context feature is generated, where the fused image includes a fused biological object of a first biological object and the second biological object, identity information and attribute information of the fused biological object is identical to the identity information and the attribute information of the first biological object, and context information of the fused biological object is identical to the context information of the second biological object.

Abstract: A computer implemented method, computer system and computer program product are provided for data classification. According to the method, an original data is received by one or more processing units. A classification of the original data with a trained classifier is determined by one or more processing units, wherein the classifier is trained by a labeled data of different granularity, and the labeled data include at least one defect object.

Abstract: Techniques facilitating attention based sequential image processing are provided. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an initialization component that can perform self-attention based training on a model that comprises context information associated with a sequence of images. Images of the sequence of images can be selected during the self-attention based training. The computer executable components can also comprise a localization component that can extract local information from the images selected during the self-attention based training based on the context information. In addition, the computer executable components can also comprise an integration component that can update the model based on an end-to-end integrated attention training framework comprising the context information and the local information.

Abstract: This disclosure is related to determining an item push list for a user based on a reinforcement learning model. In one aspect, a method includes obtaining M first item lists that have been predetermined for a first user. Each first item list includes i?1 items. For each first item list, an ith state feature vector is obtained. The ith state feature vector includes a static feature and a dynamic feature. The ith state feature vector is provided as input to the reinforcement machine learning model. The reinforcement model outputs a weight vector including weights of sorting features. A sorting feature vector of each item in a candidate item set corresponding to the first item list is obtained. The sorting feature vector includes feature values of sorting features. M updated item lists are determined for the first item lists based on a score for each item in M candidate item sets.

Abstract: Methods, systems, and storage components for utilizing a deep neural network(s) for classifying at least one medical image. A deep neural network (DNN) can be configured by an image processing component to contain at least one revived neuron, where the revived neuron has an adjusted value based on a focus-learning function that is transferred to the DNN by the image processing component, where the focus-learning function provides the adjustment by updating the DNN with data that contains a corrected classification with respect to at least one normal image sample derived from a medical image, and where the correction is based on the focus-learning function comparing an annotation associated with an abnormal image sample derived from the medical image to another annotation associated with the at least one normal image.

Abstract: Embodiments of the present disclosure disclose a method and an apparatus. The apparatus includes interface circuitry and processing circuitry. The processing circuitry obtains attribute information of multimedia information associated with a multimedia provider. The attribute information includes website information of a website used to display the multimedia information and identifier (ID) information associated with the multimedia information. The processing circuitry generates a machine-readable identification code to be scanned by a user device of a user based on at least the website information. Based on the ID information, the processing circuitry obtains a first image corresponding to the multimedia information. The processing circuitry determines an image area in the first image in which the machine-readable identification code is to be inserted. The processing circuitry inserts the machine-readable identification code into the image area to generate a second image.

Abstract: A method, system, and computer program product, include receiving a first input at a first element among a plurality of elements associated with at least one electronic document, determining a second element associated with the first element from the plurality of elements based on predetermined relations of the plurality of elements, and causing a view to be displayed together with an electronic document including the first element, the view at least including the second element.

Abstract: This disclosure is related to determining an item push list for a user based on a reinforcement learning model. In one aspect, a method includes obtaining M first item lists that have been predetermined for a first user. Each first item list includes i-1 items. For each first item list, an ith state feature vector is obtained. The ith state feature vector includes a static feature and a dynamic feature. The ith state feature vector is provided as input to the reinforcement machine learning model. The reinforcement model outputs a weight vector including weights of sorting features. A sorting feature vector of each item in a candidate item set corresponding to the first item list is obtained. The sorting feature vector includes feature values of sorting features. M updated item lists are determined for the first item lists based on a score for each item in M candidate item sets.

Abstract: A method and system of detecting abnormalities captured in a medical image are provided. A medical image is received having one or more lesions and one or more interferences. One or more convolution layers are applied to the medical image to create one or more feature maps, respectively. A region proposal network (RPN) is applied to each of the one or more feature maps to create a bounding box around each lesion and interference. For each bounding box, an object in the bounding box is classified as a lesion, an interference, or a background. Each object that is incorrectly classified as a lesion but determined to be an interference, is stored as a hard-negative example to be part of a training set for a next application of the RPN.

Abstract: A method, system, and computer program product, include receiving a request for registration from a service provider, upon the service receiver having authorized the request for registration, registering characteristic information of the service call in a user device of a service receiver, and upon a lapse of time, deregistering the characteristic information from the user device.

Abstract: A computer implemented method, computer system and computer program product are provided for data classification. The method includes receiving original data, wherein the original data includes at least one object in a first condition. The method also includes receiving generated data from a generator based on the original data, wherein the generated data includes the at least one object in a second condition, the generator trained by training data of the first condition and training data of the second condition. The method further includes determining a classification of the at least one object with a classifier based on the original data and the generated data, the classifier trained by labeled data of the first condition and more training data of the second condition that is generated based on the labeled data by the trained generator, wherein the labeled data includes the at least one object.

Abstract: Techniques facilitating attention based sequential image processing are provided. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an initialization component that can perform self-attention based training on a model that comprises context information associated with a sequence of images. Images of the sequence of images can be selected during the self-attention based training. The computer executable components can also comprise a localization component that can extract local information from the images selected during the self-attention based training based on the context information. In addition, the computer executable components can also comprise an integration component that can update the model based on an end-to-end integrated attention training framework comprising the context information and the local information.

Abstract: Techniques facilitating attention based sequential image processing are provided. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an initialization component that can perform self-attention based training on a model that comprises context information associated with a sequence of images. Images of the sequence of images can be selected during the self-attention based training. The computer executable components can also comprise a localization component that can extract local information from the images selected during the self-attention based training based on the context information. In addition, the computer executable components can also comprise an integration component that can update the model based on an end-to-end integrated attention training framework comprising the context information and the local information.

Abstract: A computer implemented method, computer system and computer program product are provided for data classification. According to the method, an original data is received by one or more processing units. A classification of the original data with a trained classifier is determined by one or more processing units, wherein the classifier is trained by a labeled data of different granularity, and the labeled data include at least one defect object.

Abstract: A method and system of detecting abnormalities captured in a medical image are provided. A medical image is received having one or more lesions and one or more interferences. One or more convolution layers are applied to the medical image to create one or more feature maps, respectively. A region proposal network (RPN) is applied to each of the one or more feature maps to create a bounding box around each lesion and interference. For each bounding box, an object in the bounding box is classified as a lesion, an interference, or a background. Each object that is incorrectly classified as a lesion but determined to be an interference, is stored as a hard-negative example to be part of a training set for a next application of the RPN.

Abstract: A computer implemented method, computer system and computer program product are provided for data classification. The method includes receiving original data, wherein the original data includes at least one object in a first condition. The method also includes receiving generated data from a generator based on the original data, wherein the generated data includes the at least one object in a second condition, the generator trained by training data of the first condition and training data of the second condition. The method further includes determining a classification of the at least one object with a classifier based on the original data and the generated data, the classifier trained by labeled data of the first condition and more training data of the second condition that is generated based on the labeled data by the trained generator, wherein the labeled data includes the at least one object.

Abstract: Methods, systems, and storage components for utilizing a deep neural network(s) for classifying at least one medical image. A deep neural network (DNN) can be configured by an image processing component to contain at least one revived neuron, where the revived neuron has an adjusted value based on a focus-learning function that is transferred to the DNN by the image processing component, where the focus-learning function provides the adjustment by updating the DNN with data that contains a corrected classification with respect to at least one normal image sample derived from a medical image, and where the correction is based on the focus-learning function comparing an annotation associated with an abnormal image sample derived from the medical image to another annotation associated with the at least one normal image.

Abstract: A method, system, and computer program product, include receiving a first input at a first element among a plurality of elements associated with at least one electronic document, determining a second element associated with the first element from the plurality of elements based on predetermined relations of the plurality of elements, and causing a view to be displayed together with an electronic document including the first element, the view at least including the second element.

Abstract: Systems, computer-implemented methods and/or computer program products that facilitate hospital department selection are provided. In one embodiment, a computer-implemented method comprises: employing, by a system operatively coupled to a processor, machine learning to train a model on data, wherein the data comprises patient data for a patient, hospital department designation associated with the patient and clinical data relating to a patient outcome, and wherein the model is trained to evaluate the hospital department designation associated with the patient based on the clinical data relating to the patient outcome; generating, by the system, a classification by classifying the patient into hospital department; and comparing, by the system, the model to the classification to provide a hospital department selection for the patient.

Abstract: Techniques that facilitate improved medical condition diagnostics are provided. An example embodiment can include a device. The device can include a memory that stores computer executable components and a processor. The processor can execute the computer executable components stored in the memory. The computer executable components can include training logic component and a determination logic component. The training logic component can generate a prediction model. The prediction model can generate \predict diagnosis based on electronic healthcare record data and image data of a known patient. The determination logic component can determine whether the predicted diagnosis exceeds an accuracy threshold value.