JP7411126B2 - 時空間的アテンションモデルに基づく多時相ct画像分類システム及び構築方法 - Google Patents
時空間的アテンションモデルに基づく多時相ct画像分類システム及び構築方法 Download PDFInfo
- Publication number
- JP7411126B2 JP7411126B2 JP2023007862A JP2023007862A JP7411126B2 JP 7411126 B2 JP7411126 B2 JP 7411126B2 JP 2023007862 A JP2023007862 A JP 2023007862A JP 2023007862 A JP2023007862 A JP 2023007862A JP 7411126 B2 JP7411126 B2 JP 7411126B2
- Authority
- JP
- Japan
- Prior art keywords
- layer
- vector
- attention
- image
- temporal
- 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
- 238000010276 construction Methods 0.000 title description 4
- 239000013598 vector Substances 0.000 claims description 202
- 230000002123 temporal effect Effects 0.000 claims description 56
- 239000011159 matrix material Substances 0.000 claims description 52
- 230000006870 function Effects 0.000 claims description 43
- 238000010606 normalization Methods 0.000 claims description 15
- 206010028980 Neoplasm Diseases 0.000 claims description 13
- 230000003111 delayed effect Effects 0.000 claims description 8
- 238000012549 training Methods 0.000 claims description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims 1
- 238000002591 computed tomography Methods 0.000 description 109
- 238000000034 method Methods 0.000 description 16
- 210000004185 liver Anatomy 0.000 description 14
- 206010073071 hepatocellular carcinoma Diseases 0.000 description 10
- 231100000844 hepatocellular carcinoma Toxicity 0.000 description 10
- 230000003902 lesion Effects 0.000 description 10
- 201000007450 intrahepatic cholangiocarcinoma Diseases 0.000 description 9
- 201000007270 liver cancer Diseases 0.000 description 9
- 208000014018 liver neoplasm Diseases 0.000 description 9
- 238000003745 diagnosis Methods 0.000 description 8
- 208000006990 cholangiocarcinoma Diseases 0.000 description 7
- 238000013527 convolutional neural network Methods 0.000 description 7
- 201000011510 cancer Diseases 0.000 description 6
- 238000013135 deep learning Methods 0.000 description 6
- 238000002347 injection Methods 0.000 description 6
- 239000007924 injection Substances 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 239000002872 contrast media Substances 0.000 description 3
- 238000002059 diagnostic imaging Methods 0.000 description 3
- 201000010099 disease Diseases 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000003058 natural language processing Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 206010027476 Metastases Diseases 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000013145 classification model Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000009401 metastasis Effects 0.000 description 2
- 206010061289 metastatic neoplasm Diseases 0.000 description 2
- 210000000056 organ Anatomy 0.000 description 2
- 239000000047 product Substances 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000005728 strengthening Methods 0.000 description 2
- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 description 1
- 241000282412 Homo Species 0.000 description 1
- 208000009443 Vascular Malformations Diseases 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 208000009956 adenocarcinoma Diseases 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000000013 bile duct Anatomy 0.000 description 1
- 210000003445 biliary tract Anatomy 0.000 description 1
- 230000000740 bleeding effect Effects 0.000 description 1
- 230000017531 blood circulation Effects 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 230000002860 competitive effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 210000000981 epithelium Anatomy 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000010253 intravenous injection Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000007449 liver function test Methods 0.000 description 1
- 210000004072 lung Anatomy 0.000 description 1
- 230000003211 malignant effect Effects 0.000 description 1
- 210000005075 mammary gland Anatomy 0.000 description 1
- 230000000873 masking effect Effects 0.000 description 1
- 230000001394 metastastic effect Effects 0.000 description 1
- 230000017074 necrotic cell death Effects 0.000 description 1
- 210000001672 ovary Anatomy 0.000 description 1
- 210000000496 pancreas Anatomy 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000010882 preoperative diagnosis Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 210000002784 stomach Anatomy 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 210000001519 tissue Anatomy 0.000 description 1
- 238000011426 transformation method Methods 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
- 210000004291 uterus Anatomy 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/003—Reconstruction from projections, e.g. tomography
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06N3/048—Activation functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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; CALCULATING OR 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; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20021—Dividing image into blocks, subimages or windows
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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; CALCULATING OR 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]
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Molecular Biology (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Apparatus For Radiation Diagnosis (AREA)
- Image Processing (AREA)
- Image Analysis (AREA)
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210672853.4 | 2022-06-15 | ||
CN202210672853.4A CN114758032B (zh) | 2022-06-15 | 2022-06-15 | 基于时空注意力模型的多相期ct图像分类系统及构建方法 |
Publications (2)
Publication Number | Publication Date |
---|---|
JP2023183367A JP2023183367A (ja) | 2023-12-27 |
JP7411126B2 true JP7411126B2 (ja) | 2024-01-10 |
Family
ID=82336458
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
JP2023007862A Active JP7411126B2 (ja) | 2022-06-15 | 2023-01-23 | 時空間的アテンションモデルに基づく多時相ct画像分類システム及び構築方法 |
Country Status (2)
Country | Link |
---|---|
JP (1) | JP7411126B2 (zh) |
CN (1) | CN114758032B (zh) |
Families Citing this family (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116152246B (zh) * | 2023-04-19 | 2023-07-25 | 之江实验室 | 一种图像识别方法、装置、设备及存储介质 |
CN116188469A (zh) * | 2023-04-28 | 2023-05-30 | 之江实验室 | 一种病灶检测方法、装置、可读存储介质及电子设备 |
CN116206164B (zh) * | 2023-05-06 | 2023-08-18 | 之江实验室 | 基于半监督对比学习的多相期ct分类系统及构建方法 |
CN117290684A (zh) * | 2023-09-27 | 2023-12-26 | 南京拓恒航空科技有限公司 | 基于Transformer的高温干旱天气预警方法、电子设备 |
CN117808976B (zh) * | 2024-03-01 | 2024-05-24 | 之江实验室 | 一种三维模型构建方法、装置、存储介质及电子设备 |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2019079305A (ja) | 2017-10-25 | 2019-05-23 | 株式会社日立製作所 | データ分析装置、データ分析方法、およびデータ分析プログラム |
CN110443268A (zh) | 2019-05-30 | 2019-11-12 | 杭州电子科技大学 | 一种基于深度学习的肝癌ct图像良性恶性分类方法 |
JP2020506466A (ja) | 2017-05-23 | 2020-02-27 | グーグル エルエルシー | アテンションベースのシーケンス変換ニューラルネットワーク |
JP2020087127A (ja) | 2018-11-28 | 2020-06-04 | 国立研究開発法人産業技術総合研究所 | グラフ構造を有するデータのエンコードに関するプログラム、情報処理方法及び情報処理システム |
CN111539491A (zh) | 2020-07-07 | 2020-08-14 | 点内(上海)生物科技有限公司 | 基于深度学习与注意力机制的多发性结节分类系统和方法 |
JP2021081921A (ja) | 2019-11-18 | 2021-05-27 | 株式会社Preferred Networks | データ処理装置、データ処理方法、プログラム、およびモデル |
CN113902926A (zh) | 2021-12-06 | 2022-01-07 | 之江实验室 | 一种基于自注意力机制的通用图像目标检测方法和装置 |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11972567B2 (en) * | 2018-05-29 | 2024-04-30 | The General Hospital Corporation | System and method for analyzing medical images to detect and classify a medical condition using machine-learning and a case pertinent radiology atlas |
US11158048B2 (en) * | 2019-06-28 | 2021-10-26 | Shandong University Of Science And Technology | CT lymph node detection system based on spatial-temporal recurrent attention mechanism |
CN112287978B (zh) * | 2020-10-07 | 2022-04-15 | 武汉大学 | 一种基于自注意力上下文网络的高光谱遥感图像分类方法 |
CN114399634B (zh) * | 2022-03-18 | 2024-05-17 | 之江实验室 | 基于弱监督学习的三维图像分类方法、系统、设备及介质 |
-
2022
- 2022-06-15 CN CN202210672853.4A patent/CN114758032B/zh active Active
-
2023
- 2023-01-23 JP JP2023007862A patent/JP7411126B2/ja active Active
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2020506466A (ja) | 2017-05-23 | 2020-02-27 | グーグル エルエルシー | アテンションベースのシーケンス変換ニューラルネットワーク |
JP2019079305A (ja) | 2017-10-25 | 2019-05-23 | 株式会社日立製作所 | データ分析装置、データ分析方法、およびデータ分析プログラム |
JP2020087127A (ja) | 2018-11-28 | 2020-06-04 | 国立研究開発法人産業技術総合研究所 | グラフ構造を有するデータのエンコードに関するプログラム、情報処理方法及び情報処理システム |
CN110443268A (zh) | 2019-05-30 | 2019-11-12 | 杭州电子科技大学 | 一种基于深度学习的肝癌ct图像良性恶性分类方法 |
JP2021081921A (ja) | 2019-11-18 | 2021-05-27 | 株式会社Preferred Networks | データ処理装置、データ処理方法、プログラム、およびモデル |
CN111539491A (zh) | 2020-07-07 | 2020-08-14 | 点内(上海)生物科技有限公司 | 基于深度学习与注意力机制的多发性结节分类系统和方法 |
CN113902926A (zh) | 2021-12-06 | 2022-01-07 | 之江实验室 | 一种基于自注意力机制的通用图像目标检测方法和装置 |
Also Published As
Publication number | Publication date |
---|---|
CN114758032B (zh) | 2022-09-16 |
CN114758032A (zh) | 2022-07-15 |
JP2023183367A (ja) | 2023-12-27 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
JP7411126B2 (ja) | 時空間的アテンションモデルに基づく多時相ct画像分類システム及び構築方法 | |
Zhong et al. | An attention-guided deep regression model for landmark detection in cephalograms | |
Ahmed et al. | Using Machine Learning via Deep Learning Algorithms to Diagnose the Lung Disease Based on Chest Imaging: A Survey. | |
Ukwuoma et al. | Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images | |
Pang et al. | Tumor attention networks: Better feature selection, better tumor segmentation | |
Liu et al. | Unsupervised surgical instrument segmentation via anchor generation and semantic diffusion | |
Patro et al. | SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19 | |
Althobaiti et al. | [Retracted] Deep Transfer Learning‐Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images | |
Du et al. | Segmentation and visualization of left atrium through a unified deep learning framework | |
Khademi et al. | Spatio-temporal hybrid fusion of cae and swin transformers for lung cancer malignancy prediction | |
Dong et al. | Learning from dermoscopic images in association with clinical metadata for skin lesion segmentation and classification | |
Ly et al. | New compact deep learning model for skin cancer recognition | |
Özbay et al. | Brain tumor detection with mRMR-based multimodal fusion of deep learning from MR images using Grad-CAM | |
Limeros et al. | GAN-based generative modelling for dermatological applications--comparative study | |
Rasoulian et al. | Weakly supervised intracranial hemorrhage segmentation using hierarchical combination of attention maps from a swin transformer | |
Yong et al. | Comparative study of encoder-decoder-based convolutional neural networks in cartilage delineation from knee magnetic resonance images | |
Toikkanen et al. | ReSGAN: Intracranial hemorrhage segmentation with residuals of synthetic brain CT scans | |
Akash Guna et al. | U-net xception: A two-stage segmentation-classification model for covid detection from lung ct scan images | |
Das et al. | Attention-UNet architectures with pretrained backbones for multi-class cardiac MR image segmentation | |
US20220287647A1 (en) | Disease classification by deep learning models | |
Bodapati | Enhancing brain tumor diagnosis using a multi-architecture deep convolutional neural network on MRI scans | |
Maram et al. | Brain tumour detection on brats 2020 using u-net | |
Wang et al. | MSAMS-Net: accurate lung lesion segmentation from COVID-19 CT images | |
Shaji et al. | Brain Tumor Segmentation Using Modified Double U-Net Architecture | |
Li et al. | Feature pyramid based attention for cervical image classification |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
A621 | Written request for application examination |
Free format text: JAPANESE INTERMEDIATE CODE: A621 Effective date: 20230123 |
|
A871 | Explanation of circumstances concerning accelerated examination |
Free format text: JAPANESE INTERMEDIATE CODE: A871 Effective date: 20230123 |
|
A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20230217 |
|
A131 | Notification of reasons for refusal |
Free format text: JAPANESE INTERMEDIATE CODE: A131 Effective date: 20230323 |
|
A601 | Written request for extension of time |
Free format text: JAPANESE INTERMEDIATE CODE: A601 Effective date: 20230622 |
|
A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20230822 |
|
A131 | Notification of reasons for refusal |
Free format text: JAPANESE INTERMEDIATE CODE: A131 Effective date: 20230905 |
|
A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20231204 |
|
TRDD | Decision of grant or rejection written | ||
A01 | Written decision to grant a patent or to grant a registration (utility model) |
Free format text: JAPANESE INTERMEDIATE CODE: A01 Effective date: 20231215 |
|
A61 | First payment of annual fees (during grant procedure) |
Free format text: JAPANESE INTERMEDIATE CODE: A61 Effective date: 20231222 |
|
R150 | Certificate of patent or registration of utility model |
Ref document number: 7411126 Country of ref document: JP Free format text: JAPANESE INTERMEDIATE CODE: R150 |