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公开(公告)号：US12086982B2
公开(公告)日：2024-09-10
申请号：US17612566
申请日：2020-10-30
发明人： Ge Li , Guanju Cheng , Chan Zeng , Peng Gao , Guotong Xie
CPC分类号： G06T7/0012 , G06T7/11 , G06T7/12 , G06T7/62 , G06T2207/20084 , G06T2207/30041
摘要： The present disclosure relates to an artificial intelligence field using a neural network, and publishes a method for confirming a cup-disc ratio based on a neural network, an apparatus, a computer device, and a computer readable storage medium. The method includes: obtaining a retinal image, and detecting an optical disc region in the retinal image to obtain the optical disc region; inputting the optical disc region into a pre-trained neural network to obtain a prediction cup-disc ratio and segment images of an optical cup and an optical disc; computing a cup-disc ratio based on the segment images of the optical cup and the optical disc; and confirming a practical cup-disc ratio based on the prediction cup-disc ratio and the computed cup-disc ratio.
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2.发明授权公开(公告)号：US12008319B2
公开(公告)日：2024-06-11
申请号：US17613506
申请日：2020-11-30
发明人： Xiang Liu , Xiuling Chen
IPC分类号： G06F40/289
CPC分类号： G06F40/289
摘要： Disclosed are a method and apparatus for selecting answers to idiom fill-in-the-blank questions, a computer device, and a storage medium. The method includes: obtaining a question text of idiom fill-in-the-blank questions, the question text including a fill-in-the-blank text and n candidate idioms, and the fill-in-the-blank text including m fill-in-the-blanks to be filled in with the candidate idioms; obtaining an explanatory text of all the candidate idioms; obtaining, through an idiom selection fill-in-the-blank model, a confidence that each fill-in-the-blank is filled in with each candidate idiom; selecting m idioms from the n candidate idioms to form multiple groups of answers; calculating a sum of the confidences that the fill-in-the-blanks are filled in with the candidate idioms in each group of answers; and obtaining a group of answers with the highest confidence sum as answers to the idiom fill-in-the-blank questions. The present application implements answers to idiom fill-in-the-blank questions with high accuracy.
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公开(公告)号：US11978215B2
公开(公告)日：2024-05-07
申请号：US17539860
申请日：2021-12-01
发明人： Yang Liu , Chengfen Zhang , Bin Lv , Chuanfeng Lv
CPC分类号： G06T7/143 , G06T7/194 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084 , G06T2207/30041
摘要： A device and method for glaucoma auxiliary diagnosis, and a non-transitory storage medium are provided. The device includes an obtaining unit and a processing unit. The obtaining unit is configured to obtain a color fundus image of a patient. The processing unit is configured to perform feature extraction on the color fundus image to obtain a first feature map. The processing unit is further configured to perform image segmentation on the color fundus image according to the first feature map to obtain an optic disc image in the color fundus image, where the optic disc image corresponds to an optic disc area in the color fundus image. The processing unit is further configured to perform feature extraction on the optic disc image and the color fundus image according to the first feature map to obtain a probability that the patient has glaucoma.
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4.发明授权公开(公告)号：US11961227B2
公开(公告)日：2024-04-16
申请号：US17168884
申请日：2021-02-05
发明人： Yue Wang , Bin Lv , Chuanfeng Lv
IPC分类号： G06T7/00 , A61B5/00 , A61B6/03 , G06F18/21 , G06F18/214 , G06F18/2415 , G06F18/25 , G06T7/11 , G06T7/136 , G06T7/187 , G06T7/73 , G06V10/764 , G06V10/774 , G06V10/776 , G06V10/80 , G06V10/82
CPC分类号： G06T7/0012 , A61B5/0066 , A61B5/4887 , A61B5/7267 , A61B5/7275 , A61B6/032 , G06F18/2148 , G06F18/217 , G06F18/2415 , G06F18/253 , G06T7/11 , G06T7/136 , G06T7/187 , G06T7/73 , G06V10/764 , G06V10/774 , G06V10/776 , G06V10/806 , G06V10/82 , G06T2207/20081 , G06T2207/30096 , G06V2201/03
摘要： A method for detecting and locating a lesion in a medical image is provided. A target medical image of a lesion is obtained and input into a deep learning model to obtain a target sequence. A first feature map output from the last convolution layer in the deep learning model is extracted. A weight value of each network unit corresponding to each preset lesion type in a fully connected layer is extracted. For each preset lesion type, a fusion feature map is calculated according to the first feature map and the corresponding weight value and resampled to the size of the target medical image to generate a generic activation map. The maximum connected area in each generic activation map is determined, and a mark border surrounding the maximum connected area is created. A mark border corresponding to each preset lesion type is added to the target medical image.
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公开(公告)号：US11941087B2
公开(公告)日：2024-03-26
申请号：US17165640
申请日：2021-02-02
发明人： Xiuming Yu , Wei Wang , Jing Xiao
IPC分类号： G06F16/00 , G06F7/24 , G06F18/213 , G06F18/214 , G06F18/22 , G06F18/2413 , G06F18/2431
CPC分类号： G06F18/24137 , G06F7/24 , G06F18/213 , G06F18/214 , G06F18/22 , G06F18/2431
摘要： Provided is an unbalanced sample data preprocessing method, which includes: a data acquisition request is received and initial data is acquired according to the data acquisition request, and the initial data is classified according to a preset classification rule to obtain first-class sample sets and second-class sample sets; characteristics of K first sample points extracted are analyzed to obtain a new data characteristic of the first-class sample sets; a new data label of the first-class sample sets is generated according to a first label corresponding to the first-class sample sets; a ratio between the number of first-class sample sets and the number of second-class sample sets is calculated; and new data of the first-class sample sets is generated according to the new data characteristic and the new data label, and the amount of new data is adjusted according to the ratio to increase the number of first-class sample sets.
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公开(公告)号：US11790528B2
公开(公告)日：2023-10-17
申请号：US17165369
申请日：2021-02-02
发明人： Jiawen Yao , Ling Zhang , Le Lu
IPC分类号： G06T7/00 , G16H30/40 , G06V10/46 , G06F18/213 , G06F18/214
CPC分类号： G06T7/0014 , G06F18/213 , G06F18/2148 , G06V10/462 , G16H30/40 , G06T2207/20084 , G06T2207/30096
摘要： A preoperative survival prediction method and a computing device applying the method include constructing a data seta according to a plurality of enhanced medical images and a resection margin of each enhanced medical image and obtaining a plurality of training data sets from the constructed data set. For each training data set, multi-task prediction models are trained. A target multi-task prediction model is selected from the plurality, and a resection margin prediction value and a survival risk prediction value are obtained by predicting an enhanced medical image to be measured through the target multi-task prediction model. The multi-task prediction model more effectively captures the changes over time of the tumor in multiple stages, so as to enable a joint prediction of a resection margin prediction value and a survival risk prediction value.
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公开(公告)号：US11790499B2
公开(公告)日：2023-10-17
申请号：US17167075
申请日：2021-02-03
发明人： Jinlun Huang , Donggen Xiong
IPC分类号： G06T5/00 , G06N3/08 , G06V30/40 , G06V30/413 , G06V30/19 , G06F18/22 , G06F18/214
CPC分类号： G06T5/009 , G06F18/214 , G06F18/22 , G06N3/08 , G06T5/006 , G06V30/19147 , G06V30/19173 , G06V30/40 , G06V30/413 , G06T2207/10024 , G06T2207/30176
摘要： A certificate image extraction method, including: step S101, obtaining an original image containing a certificate image, wherein the original image is obtained by a camera device by means of photographing; step S102, performing white balance processing on the original image to obtain a balance image according to component values of pixel points in the original image in red, green and blue color components; step S103, determining a position of the certificate image in the balance image according to a pre-trained certificate feature model; wherein the certificate feature model is obtained by training based on historical certificate images, a certificate image model and a preset initial weight value; and step S104, extracting the certificate image from the balance image according to the position of the certificate image. By performing the certificate image extraction method, the accuracy of extracting the certificate image from the original image is improved.
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8.发明授权公开(公告)号：US11770199B2
公开(公告)日：2023-09-26
申请号：US16095344
申请日：2018-02-26
发明人： Liangling Yu , Congjian Dai , Huangwei Fang , Weiwei Ye , Xiaohua Li
IPC分类号： H04J3/06 , G06F11/14 , H04L43/04 , H04B17/318
CPC分类号： H04J3/06 , G06F11/1443 , H04B17/318 , H04L43/04 , G06F2201/805
摘要： Embodiments of the present application disclose a traffic data self-recovery processing method, including: monitoring an operation result of traffic data synchronization operation of a target system; repeatedly performing the traffic data synchronization operation of the target system until the traffic data synchronization is successful or cumulative number of traffic data synchronization failures exceed a failure frequency threshold, if the monitored operation result is that the traffic data synchronization is failed; clearing the cumulative number if the monitored operation result is that the traffic data synchronization is successful; stopping the traffic data synchronization operation of the target system and sending out a message indicative of the traffic data synchronization failure if the cumulative number of traffic data synchronization failures exceeds the failure frequency threshold, wherein the failure frequency threshold is determined by current network signal intensity of the target system and is in a positive correlation with current network signal intensity.
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公开(公告)号：US11734954B2
公开(公告)日：2023-08-22
申请号：US17266587
申请日：2019-11-12
发明人： Moyan Zhao , Hongwei Wang
CPC分类号： G06V40/171 , G06V10/751 , G06V40/172
摘要： A face recognition method includes: detecting keypoints when receiving a first face image; acquiring a recognition score of each detectable keypoint and serial numbers of missing keypoints; acquiring a plurality of target keypoints in the plurality of detectable keypoints having a predetermined face feature association relationship with the missing keypoints when the influence score is higher than a predetermined score threshold; acquiring a target face feature template having a degree of position combination with the plurality of target keypoints greater than a predetermined combination degree threshold; and stitching the target face feature template and the plurality of target keypoints on the first face image to obtain a second face image so as to detect all the keypoints according to the second face image for performing the face recognition.
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10.发明授权公开(公告)号：US11710571B2
公开(公告)日：2023-07-25
申请号：US17264299
申请日：2019-08-30
发明人： Wenxiao Jia , Kewei Tan , Xiang Li , Guotong Xie
IPC分类号： G16H50/50 , G06N3/0442 , G16H50/20 , G16H50/30 , G06N3/045
CPC分类号： G16H50/50 , G06N3/045 , G06N3/0442 , G16H50/20 , G16H50/30
摘要： A long short-term memory (LSTM) model-based disease prediction method and apparatus, a computer device, and a storage medium are provided. The method includes: obtaining first medical data of a target object and second medical data of an associated object; inputting the first medical data and the second medical data into a first LSTM network in the LSTM model, to obtain a hidden state vector sequence in the first LSTM network; inputting the hidden state vector sequence into a second LSTM network for operation, to obtain a disease prediction result; selecting a predicted disease with an incidence rate higher than a preset threshold, and recording the predicted disease as a designated disease, and obtaining, based on a preset disease association network, an associated disease directly connected to the designated disease; and outputting the disease prediction result and the associated disease, thereby improving the prediction accuracy.
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