Abstract: A method of performing distributed matrix computation using task entanglement-based coding as a method of processing a huge amount of matrix computation in a distributed manner in a distributed computing environment is provided. A main server encodes information to be transmitted to a plurality of edge devices for distributed matrix computation on the basis of task entanglement-based coding employing a Chebyshev polynomial, thereby reducing the amount of information to be transmitted. Also, when the number of computation results received from the edge devices becomes a recovery threshold, the main server immediately performs decoding to derive a matrix computation result.

Abstract: There is provided a medical image segmentation deep-learning model generation apparatus including a training data generation/allocation unit configured to generate a training dataset through a segmentation result value acquired by inputting a given medical image to an original medical image segmentation deep-learning model and a learning control unit configured to acquire temporary weights using output data corresponding to primary learning by inputting good task data and bad task data sampled from primary learning training datasets to the medical image segmentation deep-learning model and configured to update weights by adding gradients acquired using weights acquired using output data corresponding to secondary learning by inputting good task data and bad task data sampled from secondary learning training datasets to the medical image segmentation deep-learning model, wherein the primary learning and the secondary learning are repeated.

Abstract: There is provided a medical image segmentation deep-learning model generation apparatus including a training data generation/allocation unit configured to generate a training dataset through a segmentation result value acquired by inputting a given medical image to an original medical image segmentation deep-learning model and a learning control unit configured to acquire temporary weights using output data corresponding to primary learning by inputting good task data and bad task data sampled from primary learning training datasets to the medical image segmentation deep-learning model and configured to update weights by adding gradients acquired using weights acquired using output data corresponding to secondary learning by inputting good task data and bad task data sampled from secondary learning training datasets to the medical image segmentation deep-learning model, wherein the primary learning and the secondary learning are repeated.