Abstract: A machine learning method based on weakly supervised learning according to an embodiment of the present invention includes extracting feature maps about a dataset given a first type of information and not given a second type of information by using a convolutional neural network (CNN), updating the CNN by back-propagating a first error value calculated as a result of performing a task corresponding to the first type of information by using a first model, and updating the CNN by back-propagating a second error value calculated as a result of performing the task corresponding to the first type of information by using a second model different from the first model, wherein the second type of information is extracted when the task corresponding to the first type of information is performed using the second model.

Abstract: Provided are an object recognition method and apparatus which determine an object of interest included in a recognition target image using a trained machine learning model and determine an area in which the object of interest is located in the recognition target image. The object recognition method based on weakly supervised learning, performed by an object recognition apparatus, includes extracting a plurality of feature maps from a training target image given classification results of objects of interest, generating an activation map for each of the objects of interest by accumulating the feature maps, calculating a representative value of each of the objects of interest by aggregating activation values included in a corresponding activation map, determining an error by comparing classification results determined using the representative value of each of the objects of interest with the given classification results and updating a CNN-based object recognition model by back-propagating the error.

Abstract: The present invention relates to a classification apparatus for pathologic diagnosis of a medical image and a pathologic diagnosis system using the same. According to the present invention, there is provided a classification apparatus for pathologic diagnosis of a medical image, including: a feature extraction unit configured to extract feature data for an input image using a feature extraction variable; a feature vector transformation unit configured to transform the extracted feature data into a feature vector using a vector transform variable; and a vector classification unit configured to classify the feature vector using a classification variable, and to output the results of the classification of pathologic diagnosis for the input image; wherein the feature extraction unit, the feature vector transformation unit and the vector classification unit are trained based on a first tagged image, a second tagged image, and an image having no tag information.

Abstract: Provided are an object recognition method and apparatus which determine an object of interest included in a recognition target image using a trained machine learning model and determine an area in which the object of interest is located in the recognition target image. The object recognition method based on weakly supervised learning, performed by an object recognition apparatus, includes extracting a plurality of feature maps from a training target image given classification results of objects of interest, generating an activation map for each of the objects of interest by accumulating the feature maps, calculating a representative value of each of the objects of interest by aggregating activation values included in a corresponding activation map, determining an error by comparing classification results determined using the representative value of each of the objects of interest with the given classification results and updating a CNN-based object recognition model by back-propagating the error.

Abstract: Provided are a machine learning method based on weakly supervised learning includes extracting feature maps about a dataset given a first type of information and not given a second type of information by using a convolutional neural network (CNN), updating the CNN by back-propagating a first error value calculated as a result of performing a task corresponding to the first type of information by using a first model, and updating the CNN by back-propagating a second error value calculated as a result of performing the task corresponding to the first type of information by using a second model different from the first model, wherein the second type of information is extracted when the task corresponding to the first type of information is performed using the second model.

Abstract: The present invention relates to a classification apparatus for pathologic diagnosis of a medical image and a pathologic diagnosis system using the same. According to the present invention, there is provided a classification apparatus for pathologic diagnosis of a medical image, including: a feature extraction unit configured to extract feature data for an input image using a feature extraction variable; a feature vector transformation unit configured to transform the extracted feature data into a feature vector using a vector transform variable; and a vector classification unit configured to classify the feature vector using a classification variable, and to output the results of the classification of pathologic diagnosis for the input image; wherein the feature extraction unit, the feature vector transformation unit and the vector classification unit are trained based on a first tagged image, a second tagged image, and an image having no tag information.

Abstract: Provided are an asymmetric butadiene-based charge transporting compound represented by one of Formula (1) and (1?) as in claim 1, an electrophotographic photoreceptor including the asymmetric butadiene-based charge transporting compound, and an electrophotographic imaging apparatus. The electrophotographic photoreceptor including the organic photoconductive material as a charge transporting material may not cause a problem, such as partial crystallization during film formation and may have a high charge potential, a sufficient photoresponsive property with high sensitivity, and a high durability. Moreover, these properties may be all maintained even in the case of exposure to light as well as use in low-temperature environments or high-speed processes, thus having high reliability.

Abstract: The present invention relates to a cloud-based pathological analysis system and method. The present invention provides a cloud-based pathological analysis system, including: a client device coupled to a microscope, and configured to acquire an image for a tissue sample via the microscope and generate a sample image; and a cloud server coupled to the client device over a network, and configured to receive sample image data from the client device over the network and store the sample image data; wherein the cloud server analyzes the received sample image data, and transmits analysis information to the client device.

Abstract: Provided are an asymmetric butadiene-based charge transporting compound represented by one of Formula (1) and (1?) as in claim 1, an electrophotographic photoreceptor including the asymmetric butadiene-based charge transporting compound, and an electrophotographic imaging apparatus. The electrophotographic photoreceptor including the organic photoconductive material as a charge transporting material may not cause a problem, such as partial crystallization during film formation and may have a high charge potential, a sufficient photoresponsive property with high sensitivity, and a high durability. Moreover, these properties may be all maintained even in the case of exposure to light as well as use in low-temperature environments or high-speed processes, thus having high reliability.

Abstract: Example embodiments relate to a system and method for searching for a new material. The example method includes acquiring a substitution tendency matrix X including substitution tendency data of ions based on existing crystal structure data, calculating an ion property matrix U by applying a symmetric matrix factorization model to the substitution tendency matrix X, acquiring substitution tendency prediction data based on the calculated ion property matrix U, and calculating probabilities of substitution of new crystal structures based on the substitution tendency prediction data.