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公开(公告)号：US20220301152A1
公开(公告)日：2022-09-22
申请号：US17257999
申请日：2020-08-17
Applicant: Google LLC
Inventor： Jason Yim , Reena Kumari Chopra , Terry Spitz , Jim Huibrecht Winkens , Annette Ada Nkechinyere Obika , Trevor Back , Joseph R. Ledsam , Pearse A. Keane , Jeffrey De Fauw
IPC: G06T7/00 , G06V10/764 , G06V10/82 , G06T7/11 , A61B5/00
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a final progression score characterizing a likelihood that a state of a medical condition affecting eye tissue will progress to a target state in a future interval of time. In one aspect, a method comprises: obtaining: (i) an input image of eye tissue captured using an imaging modality, and (ii) a segmentation map of the eye tissue in the input image into a plurality of tissue types; providing the input image to each of one or more first classification neural networks to obtain a respective first progression score from each first classification neural network; providing the segmentation map to each of one or more second classification neural networks to obtain a respective second progression score from each second classification neural network; and generating the final progression score based on the first and second progression scores.
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2.发明授权公开(公告)号：US11954902B2
公开(公告)日：2024-04-09
申请号：US17114586
申请日：2020-12-08
Applicant: Google LLC
Inventor： Jeffrey De Fauw , Joseph R. Ledsam , Bernardino Romera-Paredes , Stanislav Nikolov , Nenad Tomasev , Samuel Blackwell , Harry Askham , Xavier Glorot , Balaji Lakshminarayanan , Trevor Back , Mustafa Suleyman , Pearse A. Keane , Olaf Ronneberger , Julien Robert Michel Cornebise
IPC: G06V10/82 , G06F18/21 , G06F18/2413 , G06F18/25 , G06T7/00 , G06T11/00 , G06V10/44 , G06V10/764 , G06V10/80
CPC classification number: G06V10/82 , G06F18/217 , G06F18/24133 , G06F18/254 , G06T7/0012 , G06T11/003 , G06V10/454 , G06V10/764 , G06V10/809 , G06T2207/10101 , G06T2207/20081 , G06T2207/20084 , G06T2207/30041 , G06V2201/03
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a final classification output for an image of eye tissue. The image is provided as input to each of one or more segmentation neural networks to obtain one or more segmentation maps of the eye tissue in the image. A respective classification input is generated from each of the segmentation maps. For each of the segmentation maps, the classification input for the segmentation map is provided as input to each of one or more classification neural networks to obtain, for each segmentation map, a respective classification output from each classification neural network. A final classification output for the image is generated from the respective classification outputs for each of the segmentation maps.
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3.发明授权公开(公告)号：US11676281B2
公开(公告)日：2023-06-13
申请号：US17380914
申请日：2021-07-20
Applicant: Google LLC
Inventor： Stanislav Nikolov , Samuel Blackwell , Jeffrey De Fauw , Bernardino Romera-Paredes , Clemens Ludwig Meyer , Harry Askham , Cian Hughes , Trevor Back , Joseph R. Ledsam , Olaf Ronneberger
CPC classification number: G06T7/11 , A61B5/7267 , A61B6/032 , A61B6/501 , A61N5/1039 , G06N3/08 , G06T7/62 , G06T2207/10081 , G06T2207/20081 , G06T2207/20084 , G06T2207/30196
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for segmenting a medical image. In one aspect, a method comprises: receiving a medical image that is captured using a medical imaging modality and that depicts a region of tissue in a body; and processing the medical image using a segmentation neural network to generate a segmentation output. The segmentation neural network can include a sequence of multiple encoder blocks and a decoder subnetwork. Training the segmentation neural network can include determining a set of error values for a segmentation channel; identifying the highest error values from the set of error values for the segmentation channel; and determining a segmentation loss based on the highest error values identified for the segmentation channel.
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Patent Agency Ranking
公开(公告)号：US11935232B2
公开(公告)日：2024-03-19
申请号：US17257999
申请日：2020-08-17
Applicant: Google LLC
Inventor： Jason Yim , Reena Kumari Chopra , Terry Spitz , Jim Huibrecht Winkens , Annette Ada Nkechinyere Obika , Trevor Back , Joseph R. Ledsam , Pearse A. Keane , Jeffrey De Fauw
IPC: A61B5/00 , G06T7/00 , G06T7/11 , G06V10/764 , G06V10/82
CPC classification number: G06T7/0012 , A61B5/4842 , A61B5/7275 , G06T7/11 , G06V10/764 , G06V10/82 , G06T2200/04 , G06T2207/10012 , G06T2207/10101 , G06T2207/20021 , G06T2207/20081 , G06T2207/20084 , G06T2207/30041 , G06V2201/03
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a final progression score characterizing a likelihood that a state of a medical condition affecting eye tissue will progress to a target state in a future interval of time. In one aspect, a method comprises: obtaining: (i) an input image of eye tissue captured using an imaging modality, and (ii) a segmentation map of the eye tissue in the input image into a plurality of tissue types; providing the input image to each of one or more first classification neural networks to obtain a respective first progression score from each first classification neural network; providing the segmentation map to each of one or more second classification neural networks to obtain a respective second progression score from each second classification neural network; and generating the final progression score based on the first and second progression scores.
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公开(公告)号：US20220254023A1
公开(公告)日：2022-08-11
申请号：US17597876
申请日：2020-06-16
Applicant: Google LLC
Inventor： Scott McKinney , Marcin Sieniek , Varun Godbole , Shravya Shetty , Natasha Antropova , Jonathan Godwin , Christopher Kelly , Jeffrey De Fauw
Abstract: A method is disclosed of processing a set of images. Each image in the set has an associated counterpart image. One or more regions of interest (ROIs) are identified in one or more of the images in the set of images. For ROI identified, a reference region is identified in the associated counterpart image. ROIs and associated reference regions are cropped out, thereby forming cropped pairs of images 1 . . . n1, that are fed to a deep learning model trained to make a prediction of probability of a state of the ROI, e.g., disease state, which generates a prediction Pi-, (i=1 . . . n) for each cropped pair. The model generates an overall prediction P from each of the predictions Pi. A visualization of the set of medical images and the associated counterpart images including the cropped pair of images is generated.
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6.发明申请公开(公告)号：US20220012891A1
公开(公告)日：2022-01-13
申请号：US17380914
申请日：2021-07-20
Applicant: Google LLC
Inventor： Stanislav Nikolov , Samuel Blackwell , Jeffrey De Fauw , Bernardino Romera-Paredes , Clemens Ludwig Meyer , Harry Askham , Cian Hughes , Trevor Back , Joseph R. Ledsam , Olaf Ronneberger
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for segmenting a medical image. In one aspect, a method comprises: receiving a medical image that is captured using a medical imaging modality and that depicts a region of tissue in a body; and processing the medical image using a segmentation neural network to generate a segmentation output, wherein the segmentation neural network comprises a sequence of multiple encoder blocks, wherein: each encoder block is a residual neural network block comprising one or more two-dimensional convolutional neural network layers, one or more three-dimensional convolutional neural network layers, or both, and each encoder block is configured to process a respective encoder block input to generate a respective encoder block output wherein a spatial resolution of the encoder block output is lower than a spatial resolution of the encoder block input.
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7.发明授权公开(公告)号：US11100647B2
公开(公告)日：2021-08-24
申请号：US16565384
申请日：2019-09-09
Applicant: Google LLC
Inventor： Stanislav Nikolov , Samuel Blackwell , Jeffrey De Fauw , Bernardino Romera-Paredes , Clemens Ludwig Meyer , Harry Askham , Cian Hughes , Trevor Back , Joseph R. Ledsam , Olaf Ronneberger
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for segmenting a medical image. In one aspect, a method comprises: receiving a medical image that is captured using a medical imaging modality and that depicts a region of tissue in a body; and processing the medical image using a segmentation neural network to generate a segmentation output, wherein the segmentation neural network comprises a sequence of multiple encoder blocks, wherein: each encoder block is a residual neural network block comprising one or more two-dimensional convolutional neural network layers, one or more three-dimensional convolutional neural network layers, or both, and each encoder block is configured to process a respective encoder block input to generate a respective encoder block output wherein a spatial resolution of the encoder block output is lower than a spatial resolution of the encoder block input.
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8.发明申请公开(公告)号：US20210118198A1
公开(公告)日：2021-04-22
申请号：US17114586
申请日：2020-12-08
Applicant: Google LLC
Inventor： Jeffrey De Fauw , Joseph R. Ledsam , Bernardino Romera-Paredes , Stanislav Nikolov , Nenad Tomasev , Samuel Blackwell , Harry Askham , Xavier Glorot , Balaji Lakshminarayanan , Trevor Back , Mustafa Suleyman , Pearse A. Keane , Olaf Ronneberger , Julien Robert Michel Cornebise
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a final classification output for an image of eye tissue. The image is provided as input to each of one or more segmentation neural networks to obtain one or more segmentation maps of the eye tissue in the image. A respective classification input is generated from each of the segmentation maps. For each of the segmentation maps, the classification input for the segmentation map is provided as input to each of one or more classification neural networks to obtain, for each segmentation map, a respective classification output from each classification neural network. A final classification output for the image is generated from the respective classification outputs for each of the segmentation maps.
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