公开(公告)号：US11593942B2

公开(公告)日：2023-02-28

申请号：US17620756

申请日：2021-04-12

申请人： NANTONG UNIVERSITY

发明人： Weiping Ding ,  Zhihao Feng ,  Ming Li ,  Ying Sun ,  Yi Zhang ,  Hengrong Ju ,  Jinxin Cao

IPC分类号： G06T7/00 ,  G06T7/10 ,  G16H30/40 ,  G06V10/82 ,  G06V10/776

摘要： Disclosed is a fully convolutional genetic neural network method for segmentation of infant brain record images. First, infant brain record image data is input and preprocessed, and genetic coding initialization is performed for parameters according to the length of a DMPGA-FCN network weight. Then, m individuals are randomly grouped into genetic native subpopulations and corresponding twin subpopulations are derived, where respective crossover probability and mutation probability pm of all the subpopulations are determined from disjoint intervals; and an optimal initialization value fa is searched for by using a genetic operator. Afterwards, fa is used as a forward propagation calculation parameter and a weighting operation is performed on the feature address featuremap. Finally, a pixel-by-pixel cross-entropy loss is calculated between predicted infant brain record images and standard segmented images to reversely update the weights, thus finally obtaining optimal weights of a network model for segmentation of the infant brain record images.

公开(公告)号：US11837329B2

公开(公告)日：2023-12-05

申请号：US17798352

申请日：2022-02-22

申请人： NANTONG UNIVERSITY

发明人： Weiping Ding ,  Yu Geng ,  Jialu Ding ,  Hengrong Ju ,  Jiashuang Huang ,  Chun Cheng ,  Ying Sun ,  Yi Zhang ,  Ming Li ,  Tingzhen Qin ,  Xinjie Shen ,  Haipeng Wang

IPC分类号： G16B40/20 ,  G16H50/20

CPC分类号： G16B40/20 ,  G16H50/20

摘要： A method for classifying multi-granularity breast cancer genes based on a double self-adaptive neighborhood radius includes large-scale gene locus data are read and normalized, and a data analysis is performed on the large-scale gene loci. An optimum value K is selected by adopting a combination of contour coefficients and a PCA dimensionality reduction visualization, and a model of information granulation is adjusted. A heuristic reduction algorithm is used to implement a multi-granularity attribute reduction of a self-adaptive neighborhood radius based on a cluster center distance and a multi-granularity attribute reduction of a neighborhood radius based on an attribute inclusion degree, and big data for breast cancer genes are classified and predicted by adopting a machine learning classification algorithm based on a SVM support vector machine.

公开(公告)号：US12094482B2

公开(公告)日：2024-09-17

申请号：US18427869

申请日：2024-01-31

申请人： Nantong University

发明人： Shibing Zhang ,  Jianrong Wu ,  Lili Guo ,  Ming Li ,  Zhihua Bao

IPC分类号： G10L21/00 ,  G10L15/06 ,  G10L15/16 ,  G10L21/0208 ,  G10L25/51

CPC分类号： G10L21/0208 ,  G10L15/063 ,  G10L15/16 ,  G10L25/51 ,  G10L2015/0633

摘要： The present application relates to a lexicon learning-based heliumspeech unscrambling method in saturation diving. In a system including divers, a correction network, and an unscrambling network, a common working language lexicon for saturation diving operation is established and is read by the divers respectively in different environments, to generate supervision signals and vector signals of the correction network, and the correction network learns heliumspeeches of the different divers at different diving depths to obtain a correction network parameter, and corrects a heliumspeech of a diver to obtain a corrected speech; and the unscrambling network learns the corrected speech and completes unscrambling of the heliumspeech.

公开(公告)号：US12131474B2

公开(公告)日：2024-10-29

申请号：US18571258

申请日：2023-05-24

申请人： NANTONG UNIVERSITY

发明人： Weiping Ding ,  Ying Sun ,  Tao Hou ,  Xinjie Shen ,  Hengrong Ju ,  Jiashuang Huang ,  Haipeng Wang ,  Tingzhen Qin ,  Yu Geng ,  Ming Li ,  Haowen Xue ,  Zhongyi Wang

IPC分类号： G06T7/00 ,  G06T5/30 ,  G06T7/12 ,  G16H15/00

CPC分类号： G06T7/0012 ,  G06T7/12 ,  G16H15/00 ,  G06T5/30 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30041

摘要： The present disclosure is a three-way U-Net method for accurately segmenting an uncertain boundary of a retinal blood vessel, includes: describing an uncertainty of a blood vessel boundary label, constructing an upper bound and a lower bound of the uncertain boundary based on the dilation operator and the erosion operator respectively to obtain a maximum value and a minimum value for the blood vessel boundary, and mapping the boundary with uncertain information into one range; combining an uncertainty representation of the boundary with a loss function, and designing a three-way loss function; training network parameters by adopting a stochastic gradient descent algorithm and utilizing a total loss of the three-way loss function; and designing and implements an auxiliary diagnosis application system for intelligently segmenting the retinal blood vessel with functions of the fundus data acquisition, the intelligent accurate segmentation and the auxiliary diagnosis for the retinal blood vessel.