Abstract: A free motion headform (FMH) impact performance prediction device using artificial intelligence includes a data processing processor configured to generate an image by extracting a pre-processed test target image, generated by pre-processing test target design data, using a pre-trained model and generate a pre-processed test target distance value by pre-processing the test target design data. The FMH input performance prediction device also includes a machine learning processor configured to concatenate the image generated by extraction on the basis of the pre-trained model and the pre-processed test target distance value and to predict impact performance using a neural network in which parameters are updated by learning based on an image obtained by pre-processing existing design data and existing impact amount data corresponding to the existing design data. The FMH input performance prediction device further includes an output processor configured to output a value learned by the machine learning processor.

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: 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: A method for automatically calculating a threshold for classifying clusters from a reference data set and processing data by using same, and a system for performing same is included herein. A data processing method using auto-thresholding includes the steps of receiving, by a data processing system, as an input, a plurality of individual numerical values included in a reference data set having two or more clusters; on the basis of each of the numerical values included in the reference data set received as an input, calculating, by the data processing system, a threshold for classifying a cluster of the reference data set; and classifying, by the data processing system, into different clusters by using the threshold, each of at least one data set to be analyzed, having a plurality of individual numerical values.