Abstract: A method for training an artificial neural network for detecting a prostate cancer from a TURP pathological image, includes: acquires a plurality of acquiring pathological images for primary training, each being a prostate needle biopsy pathological image or a radical prostatectomy pathological image; using the pathological images to primarily train an artificial neural network for determining prostate cancer; acquiring TURP pathological images; and using the TURP pathological images to secondarily train the primarily trained artificial neural network, wherein each TURP pathological image includes a non-prostate tissue region and/or a cauterized prostate tissue region, and does not include any prostate cancer lesion region.

Abstract: A bladder lesion diagnosis method using a learned neural network, and a system thereof. The bladder lesion diagnosis method using a neural network includes the steps of: receiving a unit pathological image by a bladder lesion diagnosis system; inputting, by the bladder lesion diagnosis system, the unit pathological image into a first neural network to obtain the diagnosis result of a first bladder lesion among a plurality of bladder lesions in the unit pathological image; and inputting, by the bladder lesion diagnosis system, the unit pathological image into a second neural network to obtain the diagnosis result of a second bladder lesion, other than the first bladder lesion, among the plurality of bladder lesions in the unit pathological image.

Abstract: A method for analyzing an output of a neural network that analyzes an output result of a neural network trained so as to output a disease expression probability for each biological image pixel includes, depending on whether an output result value of the neural network for each pixel is equal to or greater than a reference value, an output analysis system determines the optimal output result value which is a reference value for detecting whether a disease is expressed in the corresponding pixel; determining an optimal cut-off value for determining whether a detected lesion site is effective with respect to a detected lesion in a biological image; and, when the output analysis system receives an output result corresponding to a diagnostic biometric image to be diagnosed, performing an output analysis on the output result by using the optimal reference value and the optimal cut-off value.

Abstract: A system for searching for a pathological image includes: an autoencoder having an encoder for receiving an original pathological image and extracting a feature of the original pathological image, and a decoder for receiving the feature of the original pathological image extracted by the encoder and generating a reconstructed pathological image corresponding to the original pathological image; a diagnostic neural network for receiving the reconstructed pathological image generated by the autoencoder that has received the original pathological image, and outputting a diagnosis result of a predetermined disease; and a training module for training the autoencoder and the diagnostic neural network by inputting a plurality of training pathological images, each labeled with a diagnosis result, into the autoencoder. The autoencoder is trained by reflecting the diagnosis result of the reconstructed pathological image output from the diagnostic neural network.

Abstract: Disclosed are a method for measuring the length of a living tissue included in a slide image, and a computing system for performing same. According to one aspect of the present invention, the method comprising the steps of: segmenting the slide image into a plurality of patches having a predetermined size; generating a graph corresponding to the slide image; for each edge included in the graph, setting a weight of the edge; for each connected component of the graph including two or more nodes, detecting shortest paths between all node pairs included in the connected components and determining a longest shortest path having the longest length from among the detected shortest paths between all the node pairs; and calculating the length of the living tissue included in the slide image, on the basis of the longest shortest path of each connected component constituting the graph.

Abstract: The present disclosure relates to a neural network training method and a computing system for performing the same, and more specifically, the neural network training method, which may enhance performance of a neural network even with little training data and the computing system for performing the same by using an auto-encoder and a multiple instance training method and extracting many data instances from one data bag as instances for training.

Abstract: The present disclosure discloses a method and system for training a neural network for determining severity, and more particularly, a method and system which may effectively learn a neural network performing patch unit severity diagnosis using a pathological slide image to which a severity indication (label) is given.

Abstract: A neuron-level plasticity control (NPC) addresses the issue of catastrophic forgetting in an artificial neural network. The plasticity of a network is controlled at a neuron level rather than at a connection level during training of a new task, thereby conserving existing knowledge. The neuron-level plasticity control evaluates the importance of each neuron and applies a low training speed to integrate important neurons. In addition, a scheduled NPC (SNPC) is provided that uses training schedule information to more clearly protect important neurons.

Abstract: A system for searching for a pathological image includes: an autoencoder having an encoder for receiving an original pathological image and extracting a feature of the original pathological image, and a decoder for receiving the feature of the original pathological image extracted by the encoder and generating a reconstructed pathological image corresponding to the original pathological image; a diagnostic neural network for receiving the reconstructed pathological image generated by the autoencoder that has received the original pathological image, and outputting a diagnosis result of a predetermined disease; and a training module for training the autoencoder and the diagnostic neural network by inputting a plurality of training pathological images, each labeled with a diagnosis result, into the autoencoder. The autoencoder is trained by reflecting the diagnosis result of the reconstructed pathological image output from the diagnostic neural network.

Abstract: A method and apparatus for controlling a jump/scroll event on a terminal includes receiving a touch signal. The touch signal indicates at least one performed gesture; determining that a jump/scroll event has occurred when the performed gesture is substantially similar to at least one candidate gesture; and orienting a document to at least one jump/scroll position based on the determining.

Abstract: A method and apparatus for controlling a jump/scroll event on a terminal includes receiving a touch signal. The touch signal indicates at least one performed gesture; determining that a jump/scroll event has occurred when the performed gesture is substantially similar to at least one candidate gesture; and orienting a document to at least one jump/scroll position based on the determining.

Abstract: A method for generating search collection of query is disclosed, which can provide a search result list displayed by an optimized method of automatically generating a specific collection by each query, the method comprising obtaining a first query and search results selected by a user from a search result list generated in response to the first query; classifying the search results into one or more groups; and generating a search collection for the first query by each group.

Abstract: A method and system for calculating a relevance between words using a document set is provided. The method of calculating the relevance between words based on a document set, includes: obtaining statistical information about the words based on at least one of the words, documents, a word classification of the words, and a document classification of the documents, wherein the words and the documents are included in the document set; standardizing the statistical information; and calculating the relevance between the words based on the standardized statistical information.

Abstract: A method for generating search collection of query is disclosed, which can provide a search result list displayed by an optimized method of automatically generating a specific collection by each query, the method comprising obtaining a first query and search results selected by a user from a search result list generated in response to the first query; classifying the search results into one or more groups; and generating a search collection for the first query by each group.

Abstract: A method and system for calculating a relevance between words using a document set is provided. The method of calculating the relevance between words based on a document set, includes: obtaining statistical information about the words based on at least one of the words, documents, a word classification of the words, and a document classification of the documents, wherein the words and the documents are included in the document set; standardizing the statistical information; and calculating the relevance between the words based on the standardized statistical information.