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

IPC: G06T7/11 ,  G06T7/62 ,  A61B5/00 ,  A61B6/03 ,  A61B6/00 ,  A61N5/10 ,  G06N3/08

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.

公开(公告)号：US11302446B2

公开(公告)日：2022-04-12

申请号：US16683139

申请日：2019-11-13

Applicant: Google LLC

Inventor： Nenad Tomasev ,  Xavier Glorot ,  Jack William Rae ,  Michal Zielinski ,  Anne Mottram ,  Harry Askham ,  Andre Saraiva Nobre Dos Santos ,  Clemens Ludwig Meyer ,  Suman Ravuri ,  Ivan Protsyuk ,  Trevor Back ,  Joseph R. Ledsam ,  Shakir Mohamed

IPC: G16H50/30 ,  G16H70/60 ,  G16H50/50 ,  G16H10/60 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future adverse health events using neural networks. One of the methods includes receiving electronic health record data for a patient; generating, from the electronic health record data, an input sequence comprising a respective feature representation at each of a plurality of time window time steps, comprising, for each time window time step: determining, for each of the possible numerical features, whether the numerical feature occurred during the time window; and generating, for each of the possible numerical features, one or more presence features that identify whether the numerical feature occurred during the time window; and processing the input sequence using a neural network to generate a neural network output that characterizes a predicted likelihood that an adverse health event will occur to the patient.

公开(公告)号：US20200152333A1

公开(公告)日：2020-05-14

申请号：US16683139

申请日：2019-11-13

Applicant: Google LLC

Inventor： Nenad Tomasev ,  Xavier Glorot ,  Jack William Rae ,  Michal Zielinski ,  Anne Mottram ,  Harry Askham ,  Andre Saraiva Nobre Dos Santos ,  Clemens Ludwig Meyer ,  Suman Ravuri ,  Ivan Protsyuk ,  Trevor Back ,  Joseph R. Ledsam ,  Shakir Mohamed

IPC: G16H50/30 ,  G16H10/60 ,  G16H50/50 ,  G16H70/60 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future adverse health events using neural networks. One of the methods includes receiving electronic health record data for a patient; generating, from the electronic health record data, an input sequence comprising a respective feature representation at each of a plurality of time window time steps, comprising, for each time window time step: determining, for each of the possible numerical features, whether the numerical feature occurred during the time window; and generating, for each of the possible numerical features, one or more presence features that identify whether the numerical feature occurred during the time window; and processing the input sequence using a neural network to generate a neural network output that characterizes a predicted likelihood that an adverse health event will occur to the patient.

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

IPC: G06T7/11 ,  G06T7/62 ,  A61B5/00 ,  A61B6/03 ,  A61B6/00 ,  A61N5/10 ,  G06N3/08

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.

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

IPC: G06T7/11 ,  G06T7/62 ,  A61B5/00 ,  A61B6/03 ,  A61B6/00 ,  A61N5/10 ,  G06N3/08

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.