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    Neural network for processing graph data
    2.
    发明授权
    Neural network for processing graph data 有权

    公开(公告)号：US12086702B2

    公开(公告)日：2024-09-10

    申请号：US18303134

    申请日：2023-04-19

    Applicant: Google LLC

    Inventor： Patrick F. Riley ,  Marc Berndl

    IPC: G06N3/045 ,  G16C20/70

    CPC classification number: G06N3/045 ,  G16C20/70

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving graph data representing an input graph comprising a plurality of vertices connected by edges; generating, from the graph data, vertex input data representing characteristics of each vertex in the input graph and pair input data representing characteristics of pairs of vertices in the input graph; and generating order-invariant features of the input graph using a neural network, wherein the neural network comprises: a first subnetwork configured to generate a first alternative representation of the vertex input data and a first alternative representation of the pair input data from the vertex input data and the pair input data; and a combining layer configured to receive an input alternative representation and to process the input alternative representation to generate the order-invariant features.

    Phenotype analysis of cellular image data using a deep metric network
    4.
    发明授权
    Phenotype analysis of cellular image data using a deep metric network 有权

    公开(公告)号：US11334770B1

    公开(公告)日：2022-05-17

    申请号：US16983136

    申请日：2020-08-03

    Applicant: Google LLC

    Inventor： Dale M. Ando ,  Marc Berndl

    IPC: G06K9/62 ,  G06N3/08 ,  G06V30/262 ,  G01N33/48 ,  G01N33/20 ,  G06V20/69

    Abstract: The present disclosure relates to phenotype analysis of cellular image data using a deep metric network. One example embodiment includes a method. The method includes receiving a target image of a target biological cell having a target phenotype. The method also includes obtaining a semantic embedding associated with the target image. The semantic embedding is generated using a machine-learned, deep metric network model. Further, the method includes obtaining, for each of a plurality of candidate images of candidate biological cells each having a respective candidate phenotype, a semantic embedding associated with the respective candidate image. In addition, the method includes identifying, for each of the semantic embeddings, common morphological variations and reducing, for each of the semantic embeddings based on the identified common morphological variations, effects of nuisances. Even further, the method includes determining, by the computing device, a similarity score for each candidate image.

    Analysis of perturbed subjects using semantic embeddings
    5.
    发明授权
    Analysis of perturbed subjects using semantic embeddings 有权

    公开(公告)号：US10769501B1

    公开(公告)日：2020-09-08

    申请号：US16133542

    申请日：2018-09-17

    Applicant: Google LLC

    Inventor： Dale M. Ando ,  Marc Berndl ,  Lusann Yang ,  Michelle Dimon

    IPC: G06K9/62 ,  G06K9/72 ,  G06N3/08 ,  G01N33/20 ,  G06K9/00 ,  G01N33/48

    Abstract: The present disclosure relates to analysis of perturbed subjects using semantic embeddings. One example embodiment includes a method. The method includes applying a respective perturbation to each of a plurality of subjects in a controlled environment. The method also includes producing a respective visual representation for each of the perturbed subjects using at least one imaging modality. Further, the method includes obtaining, by a computing device for each of the respective visual representations, a corresponding semantic embedding associated with the respective visual representation. The semantic embedding associated with the respective visual representation is generated using a machine-learned, deep metric network model. In addition, the method includes classifying, by the computing device based on the corresponding semantic embedding, each of the visual representations into one or more groups.

    Phenotype analysis of cellular image data using a deep metric network
    6.
    发明授权
    Phenotype analysis of cellular image data using a deep metric network 有权

    公开(公告)号：US10467754B1

    公开(公告)日：2019-11-05

    申请号：US15808699

    申请日：2017-11-09

    Applicant: Google LLC

    Inventor： Dale M. Ando ,  Marc Berndl

    IPC: G06T7/00 ,  G06K9/00 ,  G06T11/60

    Abstract: The present disclosure relates to a phenotype analysis of cellular image data using a deep metric network. One example embodiment includes a method. The method includes receiving, by a computing device, a plurality of candidate images of candidate biological cells each having a respective candidate phenotype. The method also includes obtaining, by the computing device for each of the plurality of candidate images, a semantic embedding associated with the respective candidate image. Further, the method includes grouping, by the computing device, the plurality of candidate images into a plurality of phenotypic strata based on their respective semantic embeddings.

    PROCESSING CELL IMAGES USING NEURAL NETWORKS
    7.
    发明申请
    PROCESSING CELL IMAGES USING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20180349770A1

    公开(公告)日：2018-12-06

    申请号：US15979104

    申请日：2018-05-14

    Applicant: Google LLC

    Inventor： Philip Charles Nelson ,  Eric Martin Christiansen ,  Marc Berndl ,  Michael Frumkin

    IPC: G06N3/08 ,  G06K9/00 ,  G06N3/04 ,  G06T7/60

    CPC classification number: G06N3/08 ,  G06K9/00127 ,  G06N3/02 ,  G06N3/0454 ,  G06T7/60 ,  G06T2207/20084

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing cell images using neural networks. One of the methods includes obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images.

    Processing cell images using neural networks
    10.
    发明授权
    Processing cell images using neural networks 有权

    公开(公告)号：US11443190B2

    公开(公告)日：2022-09-13

    申请号：US16905714

    申请日：2020-06-18

    Applicant: Google LLC

    Inventor： Philip Charles Nelson ,  Eric Martin Christiansen ,  Marc Berndl ,  Michael Frumkin

    IPC: G06V20/69 ,  G06N3/08 ,  G06T7/60 ,  G06N3/04 ,  G06N3/02

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing cell images using neural networks. One of the methods includes obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images.

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