Abstract: A method for training a deep neural network according to an embodiment includes training a deep neural network model using a first data set including a plurality of labeled data and a second data set including a plurality of unlabeled data, assigning a ground-truth label value to some of the plurality of unlabeled data, updating the first data set and the second data set such that the data to which the ground-truth label value is assigned is included in the first data set, and further training the deep neural network model using the updated first data set and the updated second data set.

Abstract: A machine learning apparatus according to an embodiment includes a feature extractor configured to extract features from an object region of an image, a label processor configured to create sentence label embeddings from a sentence label corresponding to the object region, a first training data creator to extract first sub-features from a plurality of first sub-regions created by partitioning the object region, add the sentence label embeddings to the extracted first sub-features, and add the first sub-features added with the sentence label embeddings to the features of the object region, a second training data creator to extract a plurality of second sub-regions along a bounding surface of the object region, create an attention matrix from the second sub-regions, and create a training data by applying the attention matrix to the features of the object region, and a trainer to train an object detection model using the training data.

Abstract: A machine learning apparatus according to an embodiment includes a feature extractor configured to extract features from an object region of an image, a label processor configured to create sentence label embeddings from a sentence label corresponding to the object region, a first training data creator to extract first sub-features from a plurality of first sub-regions created by partitioning the object region, add the sentence label embeddings to the extracted first sub-features, and add the first sub-features added with the sentence label embeddings to the features of the object region, a second training data creator to extract a plurality of second sub-regions along a bounding surface of the object region, create an attention matrix from the second sub-regions, and create a training data by applying the attention matrix to the features of the object region, and a trainer to train an object detection model using the training data.

Abstract: A method of training a model for object classification and detection includes training a first classification model including a shared feature extractor shared by classification models and a first classifier for outputting a result of an object in a first input image based on feature values of the first input image, training a second classification model including the shared feature extractor and a second classifier for outputting a result about authenticity of a second input image based on feature values of the second input image, and training a third classification model including the shared feature extractor and a third classifier for outputting a classification result about a rotation angle of a third input image on the basis of feature values of the third input image extracted by the shared feature extractor, using a third training image set including images rotated at one or more angles.

Abstract: A method for training a deep neural network according to an embodiment includes training a deep neural network model using a first data set including a plurality of labeled data and a second data set including a plurality of unlabeled data, assigning a ground-truth label value to some of the plurality of unlabeled data, updating the first data set and the second data set such that the data to which the ground-truth label value is assigned is included in the first data set, and further training the deep neural network model using the updated first data set and the updated second data set.

Abstract: A method of training a model for object classification and detection includes training a first classification model including a shared feature extractor shared by classification models and a first classifier for outputting a result of an object in a first input image based on feature values of the first input image, training a second classification model including the shared feature extractor and a second classifier for outputting a result about authenticity of a second input image based on feature values of the second input image, and training a third classification model including the shared feature extractor and a third classifier for outputting a classification result about a rotation angle of a third input image on the basis of feature values of the third input image extracted by the shared feature extractor, using a third training image set including images rotated at one or more angles.

Abstract: Provided is a method of managing a medical metadatabase. The method includes: classifying a plurality of pieces of annotation information into annotation groups according to a predetermined classification standard; determining a common annotation area of each annotation group using annotation area information of each piece of annotation information included in each annotation group; and configuring a metadatabase which matches a patient's clinical information related to each piece of annotation information included in each annotation group to the common annotation area, wherein each piece of annotation information includes annotation area information in a medical reference image.