International Business Machines Corporation

Xiaolu Zhang

Shiwan Zhao

Li Ni Zhang

Zhong Su

Keke Cai

Dongxu Duan

Xin Zhang

Peng Gao

Bai Chen Deng

Xiu Li Li

Shi Lei Zhang

Yong Qin

Shi Wan Zhao

Guo Tong Xie

Dingli Gao

Bingzhe Wu

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.

Classifier trained with data of different granularity

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.

Attention based sequential image processing

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.

Deep image classification of medical images

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.

System, method and computer program product for electronic document display

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.

Negative sample enhanced object detection machine

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.

System, method and computer program product for service call identification

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.

Generator-to-classifier framework for object classification

A method, system, and computer program product, include determining whether a service receiver has authorized a request for registration and upon the service receiver having authorized the request for registration, registering characteristic information of the service call for the service receiver.

A method, system, and computer program product, include receiving a request for registration from a service provider, the request for registration comprising characteristic information of a service call to be initiated, determining whether the service receiver has authorized the request for registration, and upon the service receiver having authorized the request for registration, registering the characteristic information of the service call for the service receiver to enable the identification of the service call.

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Beijing, China

Xiaolu Zhang