Patents by Inventor Bernardino ROMERA PAREDES
Bernardino ROMERA PAREDES has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240127045Abstract: A method performed by one or more computers for obtaining an optimized algorithm that (i) is functionally equivalent to a target algorithm and (ii) optimizes one or more target properties when executed on a target set of one or more hardware devices. The method includes: initializing a target tensor representing the target algorithm; generating, using a neural network having a plurality of network parameters, a tensor decomposition of the target tensor that parametrizes a candidate algorithm; generating target property values for each of the target properties when executing the candidate algorithm on the target set of hardware devices; determining a benchmarking score for the tensor decomposition based on the target property values of the candidate algorithm; generating a training example from the tensor decomposition and the benchmarking score; and storing, in a training data store, the training example for use in updating the network parameters of the neural network.Type: ApplicationFiled: October 3, 2022Publication date: April 18, 2024Inventors: Thomas Keisuke Hubert, Shih-Chieh Huang, Alexander Novikov, Alhussein Fawzi, Bernardino Romera-Paredes, David Silver, Demis Hassabis, Grzegorz Michal Swirszcz, Julian Schrittwieser, Pushmeet Kohli, Mohammadamin Barekatain, Matej Balog, Francisco Jesus Rodriguez Ruiz
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Patent number: 11954902Abstract: 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.Type: GrantFiled: December 8, 2020Date of Patent: April 9, 2024Assignee: Google LLCInventors: 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
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3-D convolutional neural networks for organ segmentation in medical images for radiotherapy planning
Patent number: 11676281Abstract: 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.Type: GrantFiled: July 20, 2021Date of Patent: June 13, 2023Assignee: Google LLCInventors: Stanislav Nikolov, Samuel Blackwell, Jeffrey De Fauw, Bernardino Romera-Paredes, Clemens Ludwig Meyer, Harry Askham, Cian Hughes, Trevor Back, Joseph R. Ledsam, Olaf Ronneberger -
Patent number: 11430123Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a plurality of possible segmentations of an image. In one aspect, a method comprises: receiving a request to generate a plurality of possible segmentations of an image; sampling a plurality of latent variables from a latent space, wherein each latent variable is sampled from the latent space in accordance with a respective probability distribution over the latent space that is determined based on the image; generating a plurality of possible segmentations of the image, comprising, for each latent variable, processing the image and the latent variable using a segmentation neural network having a plurality of segmentation neural network parameters to generate the possible segmentation of the image; and providing the plurality of possible segmentations of the image in response to the request.Type: GrantFiled: May 22, 2020Date of Patent: August 30, 2022Assignee: DeepMind Technologies LimitedInventors: Simon Kohl, Bernardino Romera-Paredes, Danilo Jimenez Rezende, Seyed Mohammadali Eslami, Pushmeet Kohli, Andrew Zisserman, Olaf Ronneberger
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3-D CONVOLUTIONAL NEURAL NETWORKS FOR ORGAN SEGMENTATION IN MEDICAL IMAGES FOR RADIOTHERAPY PLANNING
Publication number: 20220012891Abstract: 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.Type: ApplicationFiled: July 20, 2021Publication date: January 13, 2022Inventors: Stanislav Nikolov, Samuel Blackwell, Jeffrey De Fauw, Bernardino Romera-Paredes, Clemens Ludwig Meyer, Harry Askham, Cian Hughes, Trevor Back, Joseph R. Ledsam, Olaf Ronneberger -
3-D convolutional neural networks for organ segmentation in medical images for radiotherapy planning
Patent number: 11100647Abstract: 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.Type: GrantFiled: September 9, 2019Date of Patent: August 24, 2021Assignee: Google LLCInventors: Stanislav Nikolov, Samuel Blackwell, Jeffrey De Fauw, Bernardino Romera-Paredes, Clemens Ludwig Meyer, Harry Askham, Cian Hughes, Trevor Back, Joseph R. Ledsam, Olaf Ronneberger -
Publication number: 20210118198Abstract: 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.Type: ApplicationFiled: December 8, 2020Publication date: April 22, 2021Inventors: 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
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Patent number: 10878601Abstract: 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.Type: GrantFiled: December 28, 2018Date of Patent: December 29, 2020Assignee: Google LLCInventors: 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
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Publication number: 20200372654Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a plurality of possible segmentations of an image. In one aspect, a method comprises: receiving a request to generate a plurality of possible segmentations of an image; sampling a plurality of latent variables from a latent space, wherein each latent variable is sampled from the latent space in accordance with a respective probability distribution over the latent space that is determined based on the image; generating a plurality of possible segmentations of the image, comprising, for each latent variable, processing the image and the latent variable using a segmentation neural network having a plurality of segmentation neural network parameters to generate the possible segmentation of the image; and providing the plurality of possible segmentations of the image in response to the request.Type: ApplicationFiled: May 22, 2020Publication date: November 26, 2020Inventors: Simon Kohl, Bernardino Romera-Paredes, Danilo Jimenez Rezende, Seyed Mohammadali Eslami, Pushmeet Kohli, Andrew Zisserman, Olaf Ronneberger
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3-D CONVOLUTIONAL NEURAL NETWORKS FOR ORGAN SEGMENTATION IN MEDICAL IMAGES FOR RADIOTHERAPY PLANNING
Publication number: 20200082534Abstract: 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.Type: ApplicationFiled: September 9, 2019Publication date: March 12, 2020Inventors: Stanislav Nikolov, Samuel Blackwell, Jeffrey De Fauw, Bernardino Romera-Paredes, Clemens Meyer, Harry Askham, Cian Hughes, Trevor Back, Joseph R. Ledsam, Olaf Ronneberger -
Publication number: 20190139270Abstract: 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.Type: ApplicationFiled: December 28, 2018Publication date: May 9, 2019Inventors: 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
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Patent number: 10198832Abstract: 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.Type: GrantFiled: June 28, 2018Date of Patent: February 5, 2019Inventors: 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
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Publication number: 20190005684Abstract: 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.Type: ApplicationFiled: June 28, 2018Publication date: January 3, 2019Inventors: 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
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Patent number: 9672416Abstract: The description relates to facial tracking. One example can include an orientation structure configured to position the wearable device relative to a user's face. The example can also include a camera secured by the orientation structure parallel to or at a low angle to the user's face to capture images across the user's face. The example can further include a processor configured to receive the images and to map the images to parameters associated with an avatar model.Type: GrantFiled: April 29, 2014Date of Patent: June 6, 2017Assignee: Microsoft Technology Licensing, LLCInventors: Cha Zhang, Zhengyou Zhang, Bernardino Romera Paredes
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Publication number: 20150310263Abstract: The description relates to facial tracking. One example can include an orientation structure configured to position the wearable device relative to a user's face. The example can also include a camera secured by the orientation structure parallel to or at a low angle to the user's face to capture images across the user's face. The example can further include a processor configured to receive the images and to map the images to parameters associated with an avatar model.Type: ApplicationFiled: April 29, 2014Publication date: October 29, 2015Applicant: MICROSOFT CORPORATIONInventors: Cha ZHANG, Zhengyou ZHANG, Bernardino ROMERA PAREDES