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1.发明申请公开(公告)号：US20220415453A1
公开(公告)日：2022-12-29
申请号：US17849269
申请日：2022-06-24
Applicant: DeepMind Technologies Limited
Inventor： Olaf Ronneberger , Marta Garnelo Abellanas , Dan Rosenbaum , Seyed Mohammadali Eslami , Jonas Anders Adler
Abstract: 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.
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2.发明授权公开(公告)号：US10198832B2
公开(公告)日：2019-02-05
申请号：US16022170
申请日：2018-06-28
Applicant: DeepMind Technologies Limited
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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公开(公告)号：US20230395186A1
公开(公告)日：2023-12-07
申请号：US18034006
申请日：2021-11-23
Applicant: DeepMind Technologies Limited
Inventor： Simon Kohl , Olaf Ronneberger , Mikhail Figurnov , Alexander Pritzel
Abstract: 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.
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公开(公告)号：US11430123B2
公开(公告)日：2022-08-30
申请号：US16881775
申请日：2020-05-22
Applicant: DeepMind Technologies Limited
Inventor： Simon Kohl , Bernardino Romera-Paredes , Danilo Jimenez Rezende , Seyed Mohammadali Eslami , Pushmeet Kohli , Andrew Zisserman , Olaf Ronneberger
Abstract: 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.
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Patent Agency Ranking
5.发明申请公开(公告)号：US20200082534A1
公开(公告)日：2020-03-12
申请号：US16565384
申请日：2019-09-09
Applicant: DeepMind Technologies Limited
Inventor： Stanislav Nikolov , Samuel Blackwell , Jeffrey De Fauw , Bernardino Romera-Paredes , Clemens 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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6.发明申请公开(公告)号：US20190139270A1
公开(公告)日：2019-05-09
申请号：US16236045
申请日：2018-12-28
Applicant: DeepMind Technologies Limited
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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7.发明公开公开(公告)号：US20230298687A1
公开(公告)日：2023-09-21
申请号：US18026376
申请日：2021-11-23
Applicant: DeepMind Technologies Limited
Inventor： Mikhail Figurnov , Alexander Pritzel , Richard Andrew Evans , Russell James Bates , Olaf Ronneberger , Simon Kohl , John Jumper
Abstract: 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.
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公开(公告)号：US20200372654A1
公开(公告)日：2020-11-26
申请号：US16881775
申请日：2020-05-22
Applicant: DeepMind Technologies Limited
Inventor： Simon Kohl , Bernardino Romera-Paredes , Danilo Jimenez Rezende , Seyed Mohammadali Eslami , Pushmeet Kohli , Andrew Zisserman , Olaf Ronneberger
Abstract: 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.
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9.发明申请公开(公告)号：US20190005684A1
公开(公告)日：2019-01-03
申请号：US16022170
申请日：2018-06-28
Applicant: DeepMind Technologies Limited
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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