CN108446730B - 一种基于深度学习的ct肺结节检测装置 - Google Patents
一种基于深度学习的ct肺结节检测装置 Download PDFInfo
- Publication number
- CN108446730B CN108446730B CN201810217568.7A CN201810217568A CN108446730B CN 108446730 B CN108446730 B CN 108446730B CN 201810217568 A CN201810217568 A CN 201810217568A CN 108446730 B CN108446730 B CN 108446730B
- Authority
- CN
- China
- Prior art keywords
- lung
- deep learning
- nodule detection
- slices
- sequence images
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/09—Supervised learning
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30061—Lung
- G06T2207/30064—Lung nodule
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/03—Recognition of patterns in medical or anatomical images
- G06V2201/032—Recognition of patterns in medical or anatomical images of protuberances, polyps nodules, etc.
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Medical Informatics (AREA)
- Biomedical Technology (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Quality & Reliability (AREA)
- Multimedia (AREA)
- Apparatus For Radiation Diagnosis (AREA)
- Image Analysis (AREA)
Priority Applications (4)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810217568.7A CN108446730B (zh) | 2018-03-16 | 2018-03-16 | 一种基于深度学习的ct肺结节检测装置 |
| US16/351,896 US10937157B2 (en) | 2018-03-16 | 2019-03-13 | Computed Tomography pulmonary nodule detection method based on deep learning |
| JP2019049066A JP6993371B2 (ja) | 2018-03-16 | 2019-03-15 | ディープラーニングに基づいたコンピュータ断層撮影肺結節検出法 |
| EP19163039.1A EP3540692A1 (en) | 2018-03-16 | 2019-03-15 | A computed tomography pulmonary nodule detection method based on deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810217568.7A CN108446730B (zh) | 2018-03-16 | 2018-03-16 | 一种基于深度学习的ct肺结节检测装置 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108446730A CN108446730A (zh) | 2018-08-24 |
| CN108446730B true CN108446730B (zh) | 2021-05-28 |
Family
ID=63194771
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810217568.7A Active CN108446730B (zh) | 2018-03-16 | 2018-03-16 | 一种基于深度学习的ct肺结节检测装置 |
Country Status (4)
| Country | Link |
|---|---|
| US (1) | US10937157B2 (https=) |
| EP (1) | EP3540692A1 (https=) |
| JP (1) | JP6993371B2 (https=) |
| CN (1) | CN108446730B (https=) |
Families Citing this family (68)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11589834B2 (en) * | 2018-03-07 | 2023-02-28 | Rensselaer Polytechnic Institute | Deep neural network for CT metal artifact reduction |
| CN109255782A (zh) * | 2018-09-03 | 2019-01-22 | 图兮深维医疗科技(苏州)有限公司 | 一种肺结节图像的处理方法、装置、设备及存储介质 |
| CN109523521B (zh) * | 2018-10-26 | 2022-12-20 | 复旦大学 | 基于多切片ct图像的肺结节分类和病灶定位方法和系统 |
| CN109636817B (zh) * | 2018-11-30 | 2020-10-30 | 华中科技大学 | 一种基于二维卷积神经网络的肺结节分割方法 |
| CN109886967A (zh) * | 2019-01-16 | 2019-06-14 | 成都蓝景信息技术有限公司 | 基于深度学习技术的肺部解剖学位置定位算法 |
| CN110189307B (zh) * | 2019-05-14 | 2021-11-23 | 慧影医疗科技(北京)有限公司 | 一种基于多模型融合的肺结节检测方法及系统 |
| JP7313192B2 (ja) * | 2019-05-27 | 2023-07-24 | キヤノンメディカルシステムズ株式会社 | 診断支援装置、及び、x線ct装置 |
| CN111062947B (zh) * | 2019-08-14 | 2023-04-25 | 深圳市智影医疗科技有限公司 | 一种基于深度学习的x光胸片病灶定位方法及系统 |
| CN110739049A (zh) * | 2019-10-10 | 2020-01-31 | 上海联影智能医疗科技有限公司 | 图像勾画方法、装置、存储介质及计算机设备 |
| CN110942446A (zh) * | 2019-10-17 | 2020-03-31 | 付冲 | 一种基于ct影像的肺结节自动检测方法 |
| CN110570425B (zh) * | 2019-10-18 | 2023-09-08 | 北京理工大学 | 一种基于深度强化学习算法的肺结节分析方法及装置 |
| CN110852350B (zh) * | 2019-10-21 | 2022-09-09 | 北京航空航天大学 | 一种基于多尺度迁移学习的肺结节良恶性分类方法和系统 |
| US11605164B2 (en) | 2019-10-22 | 2023-03-14 | Shanghai United Imaging Intelligence Co., Ltd. | Systems and methods for lung nodule evaluation |
| CN110992312B (zh) * | 2019-11-15 | 2024-02-27 | 上海联影智能医疗科技有限公司 | 医学图像处理方法、装置、存储介质及计算机设备 |
| CN110782446B (zh) * | 2019-10-25 | 2022-04-15 | 杭州依图医疗技术有限公司 | 一种确定肺结节体积的方法及装置 |
| CN112365504B (zh) * | 2019-10-29 | 2024-11-29 | 杭州脉流科技有限公司 | Ct左心室分割方法、装置、设备和存储介质 |
| CN110853011B (zh) * | 2019-11-11 | 2022-05-27 | 河北工业大学 | 用于肺结节检测的卷积神经网络模型的构建方法 |
| CN110992377B (zh) * | 2019-12-02 | 2020-12-22 | 推想医疗科技股份有限公司 | 图像分割方法、装置、计算机可读存储介质和设备 |
| KR102322995B1 (ko) * | 2019-12-10 | 2021-11-09 | (주)헬스허브 | 다이나믹 윈도우 기반 인공지능 노듈 세그먼테이션 방법 및 그 장치 |
| CN111080625B (zh) * | 2019-12-18 | 2020-12-29 | 推想医疗科技股份有限公司 | 肺部影像条索检测模型的训练方法及其训练装置 |
| CN111127482B (zh) * | 2019-12-20 | 2023-06-30 | 广州柏视医疗科技有限公司 | 基于深度学习的ct影像肺气管的分割方法及系统 |
| CN111369623B (zh) * | 2020-02-27 | 2022-11-15 | 复旦大学 | 一种基于深度学习3d目标检测的肺部ct图像识别方法 |
| CN110969623B (zh) * | 2020-02-28 | 2020-06-26 | 北京深睿博联科技有限责任公司 | 一种肺部ct多征象自动检测方法、系统、终端及存储介质 |
| CN111476766B (zh) * | 2020-03-31 | 2023-08-22 | 哈尔滨商业大学 | 基于深度学习的肺结节ct图像检测系统 |
| CN111402254B (zh) * | 2020-04-03 | 2023-05-16 | 杭州华卓信息科技有限公司 | 一种ct图像肺结节高性能自动检测方法及装置 |
| CN111539918B (zh) * | 2020-04-15 | 2023-05-02 | 复旦大学附属肿瘤医院 | 基于深度学习的磨玻璃肺结节风险分层预测系统 |
| CN111553892B (zh) * | 2020-04-23 | 2021-11-05 | 北京小白世纪网络科技有限公司 | 基于深度学习的肺结节分割计算方法、装置及系统 |
| CN111915556B (zh) * | 2020-06-22 | 2024-05-14 | 杭州深睿博联科技有限公司 | 一种基于双分支网络的ct图像病变检测方法、系统、终端及存储介质 |
| CN111862001B (zh) * | 2020-06-28 | 2023-11-28 | 微医云(杭州)控股有限公司 | Ct影像的半自动标注方法及装置、电子设备、存储介质 |
| CN111815593B (zh) * | 2020-06-29 | 2024-03-01 | 郑州大学 | 基于对抗学习的肺结节域适应分割方法、装置及存储介质 |
| CN111798425B (zh) * | 2020-06-30 | 2022-05-27 | 天津大学 | 基于深度学习的胃肠道间质瘤中核分裂象智能检测方法 |
| CN111815608B (zh) * | 2020-07-13 | 2023-08-25 | 北京小白世纪网络科技有限公司 | 基于深度学习的新冠肺炎患者康复时间预测方法及系统 |
| US20230289957A1 (en) * | 2020-07-23 | 2023-09-14 | Deep Bio Inc. | Disease diagnosis method using neural network trained by using multi-phase biometric image, and disease diagnosis system performing same |
| CN112116558A (zh) * | 2020-08-17 | 2020-12-22 | 您好人工智能技术研发昆山有限公司 | 一种基于深度学习的ct影像肺结节检测系统 |
| CN111986189B (zh) * | 2020-08-27 | 2022-12-27 | 上海市公共卫生临床中心 | 一种基于ct影像的多类别肺炎筛查深度学习装置 |
| CN112241948B (zh) * | 2020-09-23 | 2024-11-01 | 深圳视见医疗科技有限公司 | 一种自适应层厚的肺结节检测分析方法及系统 |
| CN112184657A (zh) * | 2020-09-24 | 2021-01-05 | 上海健康医学院 | 一种肺结节自动检测方法、装置及计算机系统 |
| CN112184659B (zh) * | 2020-09-24 | 2023-08-25 | 上海健康医学院 | 一种肺部图像处理方法、装置及设备 |
| CN112365498B (zh) * | 2020-12-10 | 2024-01-23 | 南京大学 | 一种针对二维图像序列中多尺度多形态目标的自动检测方法 |
| CN112634210B (zh) * | 2020-12-10 | 2024-04-09 | 重庆大学 | 一种基于三维ct影像的肺结节检测方法 |
| US11776128B2 (en) * | 2020-12-11 | 2023-10-03 | Siemens Healthcare Gmbh | Automatic detection of lesions in medical images using 2D and 3D deep learning networks |
| CN113077427B (zh) * | 2021-03-29 | 2023-04-25 | 北京深睿博联科技有限责任公司 | 一种类别预测模型的生成方法及装置 |
| CN113012144A (zh) * | 2021-04-08 | 2021-06-22 | 湘南学院附属医院 | 一种肺部肿瘤的自动勾画方法、勾画系统、计算设备和存储介质 |
| CN113269747B (zh) * | 2021-05-24 | 2023-06-13 | 浙江大学医学院附属第一医院 | 一种基于深度学习的病理图片肝癌扩散检测方法及系统 |
| CN113554655B (zh) * | 2021-07-13 | 2021-12-31 | 中国科学院空间应用工程与技术中心 | 基于多特征增强的光学遥感图像分割方法及装置 |
| CN113658105A (zh) * | 2021-07-21 | 2021-11-16 | 杭州深睿博联科技有限公司 | 一种3d肝脏病灶检测方法及装置 |
| CN113744183B (zh) * | 2021-07-27 | 2024-04-19 | 山东师范大学 | 肺结节检测方法及系统 |
| CN113658174B (zh) * | 2021-09-02 | 2023-09-19 | 北京航空航天大学 | 基于深度学习和图像处理算法的微核组学图像检测方法 |
| US11367191B1 (en) * | 2021-10-07 | 2022-06-21 | Qure.Ai Technologies Private Limited | Adapting report of nodules |
| KR102710766B1 (ko) * | 2021-10-08 | 2024-09-27 | 주식회사 피맥스 | 폐 질환 경과 추적을 위한 정보 제공 방법 및 이를 위한 장치 |
| CN113889231A (zh) * | 2021-10-15 | 2022-01-04 | 长春工业大学 | 一种基于手工特征和深度特征融合的肺结节图像处理方法 |
| CN113971728B (zh) * | 2021-10-25 | 2023-04-21 | 北京百度网讯科技有限公司 | 图像识别方法、模型的训练方法、装置、设备及介质 |
| CN113962984A (zh) * | 2021-11-15 | 2022-01-21 | 北京航空航天大学 | 基于深度学习算法的质子ct成像方法、装置和电子设备 |
| CN114119546B (zh) * | 2021-11-25 | 2025-05-30 | 推想医疗科技股份有限公司 | 检测mri影像的方法及装置 |
| CN114332132A (zh) * | 2021-12-31 | 2022-04-12 | 联影智能医疗科技(成都)有限公司 | 图像分割方法、装置和计算机设备 |
| CN114581698A (zh) * | 2022-01-20 | 2022-06-03 | 江南大学 | 一种基于空间交叉注意力机制特征融合的目标分类方法 |
| CN114677383B (zh) * | 2022-03-03 | 2024-03-15 | 西北工业大学 | 基于多任务学习的肺结节检测分割方法 |
| CN114693671B (zh) * | 2022-04-25 | 2022-11-29 | 香港中文大学(深圳) | 基于深度学习的肺结节半自动分割方法、装置、设备及介质 |
| CN115100156B (zh) * | 2022-07-01 | 2024-12-03 | 江苏康思宁医疗科技有限公司 | 一种深度学习级联ct肺结节检测方法及装置 |
| CN115393321A (zh) * | 2022-08-26 | 2022-11-25 | 南通大学 | 一种基于深度学习的多层螺旋ct对肺结核多分类检出方法 |
| CN117635519A (zh) * | 2022-08-29 | 2024-03-01 | 杭州堃博生物科技有限公司 | 基于ct图像的病灶检测方法、装置及计算机可读存储介质 |
| US20240152747A1 (en) * | 2022-11-08 | 2024-05-09 | UnitX, Inc. | Three-dimensional spatial-channel deep learning neural network |
| CN116012355B (zh) * | 2023-02-07 | 2023-11-21 | 重庆大学 | 一种基于深度学习的自适应假阳性肺结节剔除方法 |
| CN116228685B (zh) * | 2023-02-07 | 2023-08-22 | 重庆大学 | 一种基于深度学习的肺结节检测与剔除方法 |
| CN116309459B (zh) * | 2023-03-21 | 2026-01-02 | 中国人民解放军国防科技大学 | 基于改进网络的肺结节检测方法、装置、设备和存储介质 |
| CN116258717B (zh) * | 2023-05-15 | 2023-09-08 | 广州思德医疗科技有限公司 | 病灶识别方法、装置、设备和存储介质 |
| CN116664953B (zh) * | 2023-06-28 | 2024-09-13 | 北京大学第三医院(北京大学第三临床医学院) | 2.5d肺炎医学ct影像分类装置及设备 |
| CN119399466A (zh) * | 2024-10-24 | 2025-02-07 | 同济大学 | 心脏3d图像的分割网络模型、训练方法、分割方法及设备 |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106940816A (zh) * | 2017-03-22 | 2017-07-11 | 杭州健培科技有限公司 | 基于3d全连接卷积神经网络的ct图像肺结节检测系统 |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5881124A (en) * | 1994-03-31 | 1999-03-09 | Arch Development Corporation | Automated method and system for the detection of lesions in medical computed tomographic scans |
| US6141437A (en) * | 1995-11-22 | 2000-10-31 | Arch Development Corporation | CAD method, computer and storage medium for automated detection of lung nodules in digital chest images |
| US7130457B2 (en) * | 2001-07-17 | 2006-10-31 | Accuimage Diagnostics Corp. | Systems and graphical user interface for analyzing body images |
| US7058210B2 (en) | 2001-11-20 | 2006-06-06 | General Electric Company | Method and system for lung disease detection |
| CN103186703A (zh) * | 2011-12-30 | 2013-07-03 | 无锡睿影信息技术有限公司 | 一种基于胸片的计算机辅助检测肺结节的方法 |
| US10282663B2 (en) | 2015-08-15 | 2019-05-07 | Salesforce.Com, Inc. | Three-dimensional (3D) convolution with 3D batch normalization |
| CN106504232B (zh) * | 2016-10-14 | 2019-06-14 | 北京网医智捷科技有限公司 | 一种基于3d卷积神经网络的肺部结节自动检测系统 |
| CN106780460B (zh) * | 2016-12-13 | 2019-11-08 | 杭州健培科技有限公司 | 一种用于胸部ct影像的肺结节自动检测系统 |
| CA3053487A1 (en) * | 2017-02-22 | 2018-08-30 | The United States Of America, As Represented By The Secretary, Department Of Health And Human Services | Detection of prostate cancer in multi-parametric mri using random forest with instance weighting & mr prostate segmentation by deep learning with holistically-nested networks |
| US10580131B2 (en) * | 2017-02-23 | 2020-03-03 | Zebra Medical Vision Ltd. | Convolutional neural network for segmentation of medical anatomical images |
| CN107274402A (zh) * | 2017-06-27 | 2017-10-20 | 北京深睿博联科技有限责任公司 | 一种基于胸部ct影像的肺结节自动检测方法及系统 |
-
2018
- 2018-03-16 CN CN201810217568.7A patent/CN108446730B/zh active Active
-
2019
- 2019-03-13 US US16/351,896 patent/US10937157B2/en active Active
- 2019-03-15 JP JP2019049066A patent/JP6993371B2/ja active Active
- 2019-03-15 EP EP19163039.1A patent/EP3540692A1/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106940816A (zh) * | 2017-03-22 | 2017-07-11 | 杭州健培科技有限公司 | 基于3d全连接卷积神经网络的ct图像肺结节检测系统 |
Non-Patent Citations (4)
| Title |
|---|
| Contextual convolutional neural networks for lung nodule classification using Gaussian-weighted average image patches;Haeil Lee et al.;《Computer-Aided Diagnosis Medical Imaging 2017》;20170303;1013423-1-1013423-7 * |
| Three-Dimentional CT Image Segmentation by Combining 2D Fully Convolutional Network with 3D Majority Voting;Xianrong Zhou et al.;《 International Workshop on Deep Learning in Medical Image Analysis》;20160927;111-120 * |
| 基于BP神经网络的体绘制转换函数研究及应用;宫延新;《中国优秀硕士学位论文全文数据库信息科技辑》;20070315;36 * |
| 基于卷积神经网络的血管图像分割;王钏;《中国优秀硕士学位论文全文数据库信息科技辑》;20170315;37 * |
Also Published As
| Publication number | Publication date |
|---|---|
| US10937157B2 (en) | 2021-03-02 |
| JP2019193776A (ja) | 2019-11-07 |
| EP3540692A1 (en) | 2019-09-18 |
| US20190287242A1 (en) | 2019-09-19 |
| JP6993371B2 (ja) | 2022-01-13 |
| CN108446730A (zh) | 2018-08-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108446730B (zh) | 一种基于深度学习的ct肺结节检测装置 | |
| CN109886273B (zh) | 一种cmr图像分割分类系统 | |
| CN110517253B (zh) | 基于3d多目标特征学习的肺结节良恶性分类的方法 | |
| Zeng et al. | Self-supervised learning framework application for medical image analysis: a review and summary | |
| Zhang et al. | LungSeek: 3D Selective Kernel residual network for pulmonary nodule diagnosis | |
| CN113592769B (zh) | 异常图像的检测、模型的训练方法、装置、设备及介质 | |
| CN111429474A (zh) | 基于混合卷积的乳腺dce-mri图像病灶分割模型建立及分割方法 | |
| Vinta et al. | Segmentation and classification of interstitial lung diseases based on hybrid deep learning network model | |
| CN115601299A (zh) | 基于图像的肝硬化状态智能评估系统及其方法 | |
| Manikandan et al. | Segmentation and detection of pneumothorax using deep learning | |
| AlSumairi et al. | X-ray image based pneumonia classification using convolutional neural networks | |
| CN118116576B (zh) | 基于深度学习的智能化病例分析方法及系统 | |
| Wu et al. | Multi-scale multi-view model based on ensemble attention for benign-malignant lung nodule classification on chest CT | |
| WO2022227193A1 (zh) | 肝脏区域分割方法、装置、电子设备及存储介质 | |
| CN116721065B (zh) | 基于对比学习预训练的ddh超声影像分析方法 | |
| Anupama et al. | Segmentation of Skin Cancer Images Using KP-UNet | |
| CN114974558A (zh) | 一种肝细胞癌辅助筛查方法及系统 | |
| Ren et al. | A neural network for disease recognition of radiological images of pneumonia | |
| Paul et al. | Computer-Aided Diagnosis Using Hybrid Technique for Fastened and Accurate Analysis of Tuberculosis Detection with Adaboost and Learning Vector Quantization | |
| Hossain et al. | Transfer Learning Assisted Cervical Cancer Categorization from Pap Smear Images Via the Multihead Attention Technique | |
| Ara et al. | Novel approach of brain tumor segmentation using convolutional neural network hybridized with water cycle algorithm | |
| Devi et al. | Deep Hybrid Networks for Multi Modal Diagnosis of Respiratory Disorders using Medical Scans | |
| Ashreetha et al. | A Novel Approach for Deep Learning Based Brain Tumor Classification Using MATLAB and MRI Imaging | |
| Kumar et al. | Hybrid EffiNet-GRU: Integrating XAI with Deep Learning Models for Breast Cancer Prediction through Histological Image Analysis | |
| Saeed et al. | Predicting Lung Cancer Recurrence Using a Mobile Application Framework |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: Room B401, floor 4, building 1, No. 12, Shangdi Information Road, Haidian District, Beijing 100085 Applicant after: Tuxiang Medical Technology Co., Ltd Address before: 100025 C, Chaoyang District International Ocean Center, Beijing, 807 Applicant before: Beijing Tuoxiang Technology Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |