Patents by Inventor Olaf Ronneberger
Olaf Ronneberger 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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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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Publication number: 20230395186Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a structure prediction neural network that comprises an embedding neural network and a main folding neural network. According to one aspect, a method comprises: obtaining a training network input characterizing a training protein; processing the training network input using the embedding neural network and the main folding neural network to generate a main structure prediction; for each auxiliary folding neural network in a set of one or more auxiliary folding neural networks, processing at least a corresponding intermediate output of the embedding neural network to generate an auxiliary structure prediction; determining a gradient of an objective function that includes a respective auxiliary structure loss term for each of the auxiliary folding neural networks; and updating the current values of the embedding network parameters and the main folding parameters based on the gradient.Type: ApplicationFiled: November 23, 2021Publication date: December 7, 2023Inventors: Simon Kohl, Olaf Ronneberger, Mikhail Figurnov, Alexander Pritzel
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Publication number: 20230298687Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting a structure of a protein comprising one or more chains. In one aspect, a method comprises: obtaining an initial multiple sequence alignment (MSA) representation; obtaining a respective initial pair embedding for each pair of amino acids in the protein; processing an input comprising the initial MSA representation and the initial pair embeddings using an embedding neural network to generate an output that comprises a final MSA representation and a respective final pair embedding for each pair of amino acids in the protein; and determining a predicted structure of the protein using the final MSA representation, the final pair embeddings, or both.Type: ApplicationFiled: November 23, 2021Publication date: September 21, 2023Inventors: Mikhail Figurnov, Alexander Pritzel, Richard Andrew Evans, Russell James Bates, Olaf Ronneberger, Simon Kohl, John Jumper
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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 -
Publication number: 20230107505Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network to (i) generate accurate network outputs for a machine learning task and (ii) generate intermediate outputs that can be used to reliably classify out-of-distribution inputs.Type: ApplicationFiled: June 4, 2021Publication date: April 6, 2023Inventors: Rudy Bunel, Jim Huibrecht Winkens, Abhijit Guha Roy, Olaf Ronneberger, Seyed Mohammadali Eslami, Ali Taylan Cemgil, Simon Kohl
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Publication number: 20220415453Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage media. One of the methods includes obtaining a plurality of images of a macromolecule having a plurality of atoms, training a decoder neural network on the plurality of images, and after the training, generating a plurality of conformations for at least a portion of the macromolecule that each include respective three-dimensional coordinates of each of the plurality of atoms, wherein generating each conformation includes sampling a conformation latent representation from a prior distribution over conformation latent representations, processing a respective input including the sampled conformation latent representation using the decoder neural network to generate a conformation output that specifies three-dimensional coordinates of each of the plurality of atoms for the conformation, and generating the conformation from the conformation output.Type: ApplicationFiled: June 24, 2022Publication date: December 29, 2022Inventors: Olaf Ronneberger, Marta Garnelo Abellanas, Dan Rosenbaum, Seyed Mohammadali Eslami, Jonas Anders Adler
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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