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公开(公告)号：US10818066B2
公开(公告)日：2020-10-27
申请号：US15821981
申请日：2017-11-24
发明人： Kyung Won Lee , Hyo Ji Ha , Hyun Woo Han , Sung Yun Bae , Ji Hye Lee , Chang Hyung Hong , Sang Joon Son , Hyun Jung Shin
摘要： Provided is a method of visualizing a plurality of nodes respectively including a plurality of variable values for a data object. The method includes: allocating a predetermined upper limit value and a predetermined lower limit value for each of a plurality of variables to vertices of a three-dimensional polygon facing each other; respectively determining partial positions related to the variables for the nodes based on the upper limit value and the lower limit value for each of the variables, a maximum variable value and a minimum variable value for each variable from among variable values of the nodes, and the variable values of the nodes; respectively determining final positions of the nodes in the three-dimensional polygon based on the determined partial positions; and arranging the nodes in the three-dimensional polygon according to the determined final positions.
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2.发明公开公开(公告)号：US20240221955A1
公开(公告)日：2024-07-04
申请号：US18401742
申请日：2024-01-02
CPC分类号： G16H50/30 , A61B5/4088 , G16H30/40
摘要： A prospective classification device for predicting dementia that predicts a risk of a patient with mild cognitive impairment being converted to a dementia patient based on the characteristics of prognostic brain imaging data converted from a diagnostic brain imaging data and a method of operating the same are disclosed. The prospective classification device is configured to convert features of the diagnostic brain imaging data obtained at the time of diagnosis of a patient with mild cognitive impairment into features of prognostic brain imaging data corresponding to the prognostic time after the time of diagnosis using a prospective classification model.
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公开(公告)号：US20240221946A1
公开(公告)日：2024-07-04
申请号：US18401741
申请日：2024-01-02
CPC分类号： G16H50/20 , A61B5/4088 , A61B5/7275
摘要： Disclosed is a learning method and device for alzheimer prediction model based on domain adaptation performed by at least one processor including extracting a point related to an object in a learning image from the learning image for a 3D reconstruction model, obtaining a gradient map including surrounding context information in three dimensions of the point from a 3D model of the object, determining a weight of the point based on the learning image and the gradient map, and learning the 3D reconstruction model by using the weight such that the 3D model of the object is output from the 3D reconstruction model into which the learning image is input.
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公开(公告)号：US20240220575A1
公开(公告)日：2024-07-04
申请号：US18398170
申请日：2023-12-28
IPC分类号： G06F17/18
CPC分类号： G06F17/18
摘要： A domain adaptation device for longitudinal data includes a first module that generates first transformation data using a projection matrix for domain transformation and a graph matrix for data filtering, a second module that determines a domain of the first transformation data, and a third module that determines a label of the first transformation data.
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