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公开(公告)号：US20220054071A1
公开(公告)日：2022-02-24
申请号：US17516427
申请日：2021-11-01
Inventor： Mengying LEI , Zijun DENG , He ZHAO , Qingqing ZHENG , Kai MA , Yefeng ZHENG
Abstract: This disclosure discloses a method and apparatus for processing a motor MI-EEG signal and a storage medium. The method includes: inputting a source MI-EEG signal belonging to a source domain and a target MI-EEG signal belonging to a target domain to an initial feature extraction model to obtain first source MI features and first target MI features; inputting the first source MI features to an initial classification model to obtain a first classification result outputted by the initial classification model, the first classification result representing an action predicted to be performed in the source MI-EEG signal; and adjusting a model parameter of the initial feature extraction model and/or a model parameter of the initial classification model when a certain condition (set) is met.
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公开(公告)号：US20210365741A1
公开(公告)日：2021-11-25
申请号：US17397857
申请日：2021-08-09
Inventor： Yifan HU , Yefeng ZHENG
Abstract: A computer device obtains a plurality of medical images. The device generates a texture image based on image data of a region of interest in the medical images. The device extracts a local feature from the texture image using a first network model. The device extracts a global feature from the medical images using a second network model. The device fuses the extracted local feature and the extracted global feature to form a fused feature. The device performs image classification based on the fused feature.
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公开(公告)号：US20210365717A1
公开(公告)日：2021-11-25
申请号：US17388249
申请日：2021-07-29
Inventor： Shilei CAO , Renzhen WANG , Kai MA , Yefeng ZHENG
Abstract: Embodiments of this disclosure include a method and an apparatus for segmenting a medical image. The method may include obtaining a slice pair comprising two slices and performing feature extraction on each slice in the slice pair, to obtain high-level feature information and low-level feature information of the each slice in the slice pair. The method may further include segmenting a target object in the each slice according to the low-level feature information and the high-level feature information of the slice, to obtain an initial segmentation result of the each slice and fusing the low-level feature information and the high-level feature information of the slices to obtain a fused feature information. The method may further include determining correlation information between the slices according to the fused feature information and generating a segmentation result of the slice pair based on the correlation information and the initial segmentation results of the slices.
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24.发明申请公开(公告)号：US20210241027A1
公开(公告)日：2021-08-05
申请号：US17204894
申请日：2021-03-17
Inventor： Yifan HU , Yefeng ZHENG
Abstract: An image segmentation method is provided for a computing device. The method includes obtaining a general tumor image, performing tumor localization on the tumor image to obtain a candidate image indicating a position of a tumor region in the general tumor image, inputting the candidate image to a cascaded segmentation network constructed based on a machine learning model, and performing image segmentation on the general tumor region in the candidate image using a first-level segmentation network and a second-level segmentation network in the cascaded segmentation network to obtain a segmented image.
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公开(公告)号：US20250005888A1
公开(公告)日：2025-01-02
申请号：US18808070
申请日：2024-08-18
Inventor： Ruifen ZHANG , Luyan LIU , Hong WANG , Yawen HUANG , Yefeng ZHENG
IPC: G06V10/42 , G06T5/10 , G06V10/771
Abstract: An image processing method includes: obtaining, through a plurality of radio frequency coils, a plurality of pieces of corresponding undersampled frequency-domain data respectively; and performing, by using a plurality of image processing networks that are cascaded, an information supplement operation respectively on the plurality of pieces of frequency-domain data to obtain a plurality of corresponding target restored images, and determining a target reconstructed image based on the plurality of target restored images, a piece of frequency-domain data being configured for obtaining one target restored image, and an image processing network including an image restoring network, a frequency-domain complement network, and a susceptibility estimation network.
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Patent Agency Ranking
公开(公告)号：US20240412374A1
公开(公告)日：2024-12-12
申请号：US18808033
申请日：2024-08-18
Inventor： Hong LIU , Dong WEI , Donghuan LU , Liansheng WANG , Yefeng ZHENG
IPC: G06T7/10 , G06N3/0455 , G06N3/084
Abstract: This application provides a training method and apparatus for an image processing model, an electronic device, and a storage medium. The method includes: obtaining a plurality of multimodal images used as training samples, types of the multimodal images including full-modality images and missing-modality images; invoking, based on each of the multimodal images, an initialized image processing model to execute a first training task for reconstructing the full-modality image, the image processing model outputting a first full-modality reconstructed image in a process of executing the first training task; performing image completion processing on each of first full-modality reconstructed images based on the full-modality image, to obtain a full-modality template image; determining a consistency loss between a multimodal image pair and the full-modality template image; and invoking, based on each of the multimodal images, a trained image processing model to execute a second training task for segmenting each of the multimodal images, and using the consistency loss as a constraint condition in the second training task.
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27.发明申请公开(公告)号：US20240412335A1
公开(公告)日：2024-12-12
申请号：US18808030
申请日：2024-08-18
Inventor： Hong WANG , Yefeng ZHENG
Abstract: This application provides a method and an apparatus for training an artifact removal model. The method includes obtaining a reference image and a corresponding artifact image; inputting the artifact image into a plurality of sample removal models to obtain artifact removal results corresponding to the artifact image respectively output by the plurality of sample removal models; determining predicted loss values respectively corresponding to the plurality of sample removal models based on pixel differences between the artifact removal results and the reference image; inputting the predicted loss values respectively corresponding to the plurality of sample removal models into a sample weight model to generate weight parameters respectively corresponding to the plurality of predicted loss values; and training the plurality of sample removal models based on the predicted loss values and the weight parameters to obtain an artifact removal model.
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公开(公告)号：US20240355110A1
公开(公告)日：2024-10-24
申请号：US18752567
申请日：2024-06-24
Inventor： Yawen HUANG , Ziyun CAI , Dandan ZHANG , Yuexiang LI , Hong WANG , Yefeng ZHENG
IPC: G06V10/82 , G06T11/60 , G06V10/44 , G06V10/764
CPC classification number: G06V10/82 , G06T11/60 , G06V10/44 , G06V10/764
Abstract: A method for training an image classification model performed by an electronic device and includes: obtaining a plurality of sample source-domain images, a plurality of sample target-domain images, modal tagging results of the sample source-domain images, and category tagging results of the sample source-domain images; determining first category prediction results of the sample source-domain images by using a neural network model; determining first category prediction results of the sample target-domain images by using the neural network model; for a category tagging result, determining a first loss of the category tagging result based on source-domain image feature pairs corresponding to the category tagging result; and training the neural network model based on first losses of category tagging results, the first category prediction results of the sample source-domain images, and the first category prediction results of the sample target-domain images, to obtain an image classification model.
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29.发明公开公开(公告)号：US20240296567A1
公开(公告)日：2024-09-05
申请号：US18664469
申请日：2024-05-15
Inventor： Zhe XU , Donghuan LU , Yefeng ZHENG
Abstract: Disclosed are a medical image segmentation method and apparatus, a device, a storage medium, and a program product, which relate to the field of artificial intelligence (AI). The method includes: performing image segmentation on a sample medical image through a source domain segmentation model, to obtain a first segmentation result, the source domain segmentation model being obtained through training based on image data in a source domain, the sample medical image being an unannotated image in a target domain; performing image segmentation on the sample medical image through a target domain segmentation model, to obtain a second segmentation result; correcting the first segmentation result based on the second segmentation result and a segmentation confidence level of the target domain segmentation model, to obtain a corrected segmentation result; and updating training on the target domain segmentation model based on the second segmentation result and the corrected segmentation result.
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30.发明公开公开(公告)号：US20230343063A1
公开(公告)日：2023-10-26
申请号：US18216918
申请日：2023-06-30
Inventor： Zhe XU , Donghuan LU , Kai MA , Yefeng ZHENG
IPC: G06V10/26 , G06V20/70 , G06V10/82 , G06V10/77 , G06V10/74 , G06T7/194 , G06V10/776 , G06V10/774
CPC classification number: G06V10/267 , G06V20/70 , G06V10/82 , G06V10/7715 , G06V10/761 , G06T7/194 , G06V10/776 , G06V10/774 , G06T2207/20084 , G06T2207/20076 , G06T2207/20081 , G06V2201/03 , G06T2207/30096 , G06T2207/30016 , G06T2207/30084
Abstract: An image segmentation model training method includes acquiring a first image, a second image, and a labeled image of the first image; acquiring a first predicted image according to a first network model; acquiring a second predicted image according to a second network model; determining a reference image of the second image based on the second image and the labeled image of the first image; and updating a model parameter of the first network model based on the first predicted image, the labeled image, the second predicted image, and the reference image to obtain an image segmentation model.
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