CN107423571B - 基于眼底图像的糖尿病视网膜病变识别系统 - Google Patents
基于眼底图像的糖尿病视网膜病变识别系统 Download PDFInfo
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Abstract
Description
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CN201810242376.1A CN108172291B (zh) | 2017-05-04 | 2017-08-03 | 基于眼底图像的糖尿病视网膜病变识别系统 |
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CN2017103098344 | 2017-05-04 | ||
CN201710309834 | 2017-05-04 |
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CN201810242376.1A Division CN108172291B (zh) | 2017-05-04 | 2017-08-03 | 基于眼底图像的糖尿病视网膜病变识别系统 |
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CN107423571A CN107423571A (zh) | 2017-12-01 |
CN107423571B true CN107423571B (zh) | 2018-07-06 |
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CN201710653663.7A Active CN107423571B (zh) | 2017-05-04 | 2017-08-03 | 基于眼底图像的糖尿病视网膜病变识别系统 |
CN201810242376.1A Active CN108172291B (zh) | 2017-05-04 | 2017-08-03 | 基于眼底图像的糖尿病视网膜病变识别系统 |
CN202010359211.XA Active CN111493814B (zh) | 2017-05-04 | 2017-08-05 | 眼底病变的识别系统 |
CN202010359208.8A Active CN111481166B (zh) | 2017-05-04 | 2017-08-05 | 基于眼底筛查的自动识别系统 |
CN201710663218.9A Active CN108553079B (zh) | 2017-05-04 | 2017-08-05 | 基于眼底图像的病变识别系统 |
Family Applications After (4)
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CN201810242376.1A Active CN108172291B (zh) | 2017-05-04 | 2017-08-03 | 基于眼底图像的糖尿病视网膜病变识别系统 |
CN202010359211.XA Active CN111493814B (zh) | 2017-05-04 | 2017-08-05 | 眼底病变的识别系统 |
CN202010359208.8A Active CN111481166B (zh) | 2017-05-04 | 2017-08-05 | 基于眼底筛查的自动识别系统 |
CN201710663218.9A Active CN108553079B (zh) | 2017-05-04 | 2017-08-05 | 基于眼底图像的病变识别系统 |
Country Status (3)
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US (2) | US11210789B2 (zh) |
CN (5) | CN107423571B (zh) |
WO (1) | WO2018201633A1 (zh) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
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CN108172291A (zh) * | 2017-05-04 | 2018-06-15 | 深圳硅基仿生科技有限公司 | 基于眼底图像的糖尿病视网膜病变识别系统 |
Families Citing this family (67)
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CN107045720B (zh) | 2017-05-04 | 2018-11-30 | 深圳硅基仿生科技有限公司 | 基于人工神经网络的识别眼底图像病变的处理系统 |
WO2019024568A1 (zh) * | 2017-08-02 | 2019-02-07 | 上海市第六人民医院 | 眼底图像处理方法、装置、计算机设备和存储介质 |
CN108154505A (zh) * | 2017-12-26 | 2018-06-12 | 四川大学 | 基于深度神经网络的糖尿病视网膜病变检测方法及装置 |
CN108197548A (zh) * | 2017-12-27 | 2018-06-22 | 苏州体素信息科技有限公司 | 一种检测人眼屈光不正的方法、客户端和服务器 |
US11132797B2 (en) | 2017-12-28 | 2021-09-28 | Topcon Corporation | Automatically identifying regions of interest of an object from horizontal images using a machine learning guided imaging system |
CN111902071A (zh) * | 2018-01-19 | 2020-11-06 | 学校法人庆应义塾 | 诊断辅助装置、学习装置、诊断辅助方法、学习方法及程序 |
CN110097966B (zh) * | 2018-01-30 | 2021-09-14 | 中国移动通信有限公司研究院 | 一种信息提醒方法、装置及终端设备 |
CN108305251A (zh) * | 2018-02-01 | 2018-07-20 | 四川大学 | 一种乳腺癌检测方法与装置 |
EP3776353B1 (en) * | 2018-04-09 | 2023-12-13 | Koninklijke Philips N.V. | Ultrasound system with artificial neural network for retrieval of imaging parameter settings for recurring patient |
CN108615051B (zh) | 2018-04-13 | 2020-09-15 | 博众精工科技股份有限公司 | 基于深度学习的糖尿病视网膜图像分类方法及系统 |
CN108392174B (zh) * | 2018-04-19 | 2021-01-19 | 梁建宏 | 一种早产儿视网膜病变的自动检查方法及系统 |
CN108596895B (zh) * | 2018-04-26 | 2020-07-28 | 上海鹰瞳医疗科技有限公司 | 基于机器学习的眼底图像检测方法、装置及系统 |
CN108634934B (zh) * | 2018-05-07 | 2021-01-29 | 北京长木谷医疗科技有限公司 | 对脊柱矢状位图像进行处理的方法和设备 |
CN110335269A (zh) | 2018-05-16 | 2019-10-15 | 腾讯医疗健康(深圳)有限公司 | 眼底图像的类别识别方法和装置 |
CN109344808A (zh) * | 2018-07-24 | 2019-02-15 | 中山大学中山眼科中心 | 一种基于深度学习的眼部图像处理系统 |
CN109119165A (zh) * | 2018-08-27 | 2019-01-01 | 珠海为凡医疗信息技术有限公司 | 一种白内障患病风险检测方法、装置及电子设备 |
CN110875092B (zh) * | 2018-08-31 | 2023-10-20 | 福州依影健康科技有限公司 | 一种基于远程眼底筛查的健康大数据服务方法和系统 |
CN109106333A (zh) * | 2018-09-29 | 2019-01-01 | 广西南宁园丁医疗器械有限公司 | 一种自动调节式自助视力筛查系统及装置 |
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CN111199794B (zh) * | 2018-11-19 | 2024-03-01 | 复旦大学附属眼耳鼻喉科医院 | 一种适用于高度近视白内障的手术智能决策系统及其建立方法 |
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US11694335B2 (en) * | 2018-12-05 | 2023-07-04 | Stryker Corporation | Systems and methods for displaying medical imaging data |
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CN111481165A (zh) * | 2019-01-28 | 2020-08-04 | 上海市眼病防治中心 | 全自动智能眼底疾病诊断系统 |
CN111507981B (zh) * | 2019-01-31 | 2021-07-13 | 数坤(北京)网络科技股份有限公司 | 图像处理方法和装置、电子设备、计算机可读存储介质 |
CN111724450A (zh) * | 2019-03-20 | 2020-09-29 | 上海科技大学 | 基于深度学习的医学图像重构系统、方法、终端、及介质 |
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CN109998474A (zh) * | 2019-04-15 | 2019-07-12 | 上海交通大学医学院附属第九人民医院 | 一种识别血管痉挛的便携式装置及其使用方法 |
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US11915826B2 (en) | 2019-06-07 | 2024-02-27 | Welch Allyn, Inc. | Digital image screening and/or diagnosis using artificial intelligence |
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CN110729044B (zh) * | 2019-10-08 | 2023-09-12 | 腾讯医疗健康(深圳)有限公司 | 糖网病变阶段识别模型的训练方法及糖网病变识别设备 |
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CN111460991A (zh) * | 2020-03-31 | 2020-07-28 | 科大讯飞股份有限公司 | 异常检测方法、相关设备及可读存储介质 |
CN111951933B (zh) * | 2020-08-07 | 2023-01-17 | 平安科技(深圳)有限公司 | 眼底彩照图像分级方法、装置、计算机设备及存储介质 |
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WO2022072513A1 (en) | 2020-09-30 | 2022-04-07 | Ai-Ris LLC | Retinal imaging system |
CN112381821A (zh) * | 2020-12-08 | 2021-02-19 | 北京青燕祥云科技有限公司 | 一种智能手持式眼底相机以及图像分析方法 |
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CN112652392A (zh) * | 2020-12-22 | 2021-04-13 | 成都市爱迦科技有限责任公司 | 一种基于深度神经网络的眼底异常预测系统 |
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CN114782452B (zh) * | 2022-06-23 | 2022-11-01 | 中山大学中山眼科中心 | 一种荧光素眼底血管造影图像的处理系统及装置 |
CN114847871B (zh) * | 2022-07-06 | 2022-10-18 | 北京鹰瞳科技发展股份有限公司 | 用于分析被检者的眼底变化趋势的方法、系统和相关产品 |
CN115482933B (zh) * | 2022-11-01 | 2023-11-28 | 北京鹰瞳科技发展股份有限公司 | 用于对驾驶员的驾驶风险进行评估的方法及其相关产品 |
CN117274278B (zh) * | 2023-09-28 | 2024-04-02 | 武汉大学人民医院(湖北省人民医院) | 基于模拟感受野的视网膜图像病灶部位分割方法及系统 |
CN117877692A (zh) * | 2024-01-02 | 2024-04-12 | 珠海全一科技有限公司 | 一种视网膜病变个性化差异分析方法 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101743566A (zh) * | 2007-07-06 | 2010-06-16 | 布拉科成像S.P.A.公司 | 使用神经网络的图像处理 |
CN106056595A (zh) * | 2015-11-30 | 2016-10-26 | 浙江德尚韵兴图像科技有限公司 | 基于深度卷积神经网络自动识别甲状腺结节良恶性的方法 |
CN106096616A (zh) * | 2016-06-08 | 2016-11-09 | 四川大学华西医院 | 一种基于深度学习的磁共振影像特征提取及分类方法 |
CN106530295A (zh) * | 2016-11-07 | 2017-03-22 | 首都医科大学 | 一种视网膜病变的眼底图像分类方法和装置 |
Family Cites Families (19)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE10225855A1 (de) * | 2002-06-07 | 2003-12-24 | Zeiss Carl Jena Gmbh | Verfahren und Anordnung zur Auswertung von mit einer Funduskamera aufgenommenen Bildern |
JP2008206536A (ja) * | 2007-02-23 | 2008-09-11 | Iwasaki Gakuen | 網膜画像による個人認証方式 |
CN102930286B (zh) * | 2012-09-18 | 2015-06-24 | 重庆大学 | 老年痴呆症图像早期诊断系统 |
US9462945B1 (en) * | 2013-04-22 | 2016-10-11 | VisionQuest Biomedical LLC | System and methods for automatic processing of digital retinal images in conjunction with an imaging device |
US9905008B2 (en) * | 2013-10-10 | 2018-02-27 | University Of Rochester | Automated fundus image field detection and quality assessment |
EP4057215A1 (en) * | 2013-10-22 | 2022-09-14 | Eyenuk, Inc. | Systems and methods for automated analysis of retinal images |
EP3192051B1 (en) * | 2014-09-08 | 2021-12-08 | The Cleveland Clinic Foundation | Automated analysis of angiographic images |
CN104881683B (zh) * | 2015-05-26 | 2018-08-28 | 清华大学 | 基于组合分类器的白内障眼底图像分类方法及分类装置 |
CN104992183B (zh) * | 2015-06-25 | 2018-08-28 | 中国计量学院 | 自然场景中的显著目标的自动检测方法 |
CN105513077B (zh) * | 2015-12-11 | 2019-01-04 | 北京大恒图像视觉有限公司 | 一种用于糖尿病性视网膜病变筛查的系统 |
WO2018045363A1 (en) * | 2016-09-02 | 2018-03-08 | Gargeya Rishab | Screening method for automated detection of vision-degenerative diseases from color fundus images |
CN106408562B (zh) * | 2016-09-22 | 2019-04-09 | 华南理工大学 | 基于深度学习的眼底图像视网膜血管分割方法及系统 |
CN106408564B (zh) * | 2016-10-10 | 2019-04-02 | 北京新皓然软件技术有限责任公司 | 一种基于深度学习的眼底图像处理方法、装置及系统 |
CN106344005B (zh) * | 2016-10-28 | 2019-04-05 | 张珈绮 | 一种可移动心电图监测系统 |
CN106570530A (zh) * | 2016-11-10 | 2017-04-19 | 西南交通大学 | 一种眼底影像中硬性渗出提取的方法 |
US10660576B2 (en) * | 2017-01-30 | 2020-05-26 | Cognizant Technology Solutions India Pvt. Ltd. | System and method for detecting retinopathy |
CN106934798B (zh) * | 2017-02-20 | 2020-08-21 | 苏州体素信息科技有限公司 | 基于深度学习的糖尿病视网膜病变分类分级方法 |
CN107045720B (zh) * | 2017-05-04 | 2018-11-30 | 深圳硅基仿生科技有限公司 | 基于人工神经网络的识别眼底图像病变的处理系统 |
CN107423571B (zh) * | 2017-05-04 | 2018-07-06 | 深圳硅基仿生科技有限公司 | 基于眼底图像的糖尿病视网膜病变识别系统 |
-
2017
- 2017-08-03 CN CN201710653663.7A patent/CN107423571B/zh active Active
- 2017-08-03 CN CN201810242376.1A patent/CN108172291B/zh active Active
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- 2017-08-05 CN CN201710663218.9A patent/CN108553079B/zh active Active
-
2021
- 2021-11-19 US US17/455,735 patent/US20220076420A1/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101743566A (zh) * | 2007-07-06 | 2010-06-16 | 布拉科成像S.P.A.公司 | 使用神经网络的图像处理 |
CN106056595A (zh) * | 2015-11-30 | 2016-10-26 | 浙江德尚韵兴图像科技有限公司 | 基于深度卷积神经网络自动识别甲状腺结节良恶性的方法 |
CN106096616A (zh) * | 2016-06-08 | 2016-11-09 | 四川大学华西医院 | 一种基于深度学习的磁共振影像特征提取及分类方法 |
CN106530295A (zh) * | 2016-11-07 | 2017-03-22 | 首都医科大学 | 一种视网膜病变的眼底图像分类方法和装置 |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108172291A (zh) * | 2017-05-04 | 2018-06-15 | 深圳硅基仿生科技有限公司 | 基于眼底图像的糖尿病视网膜病变识别系统 |
CN108172291B (zh) * | 2017-05-04 | 2020-01-07 | 深圳硅基智能科技有限公司 | 基于眼底图像的糖尿病视网膜病变识别系统 |
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