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1.发明申请公开(公告)号：US20200066407A1
公开(公告)日：2020-02-27
申请号：US16488029
申请日：2017-02-23
Applicant: Google LLC
Inventor： Martin Christian Stumpe , Lily Peng , Yun Liu , Krishna K. Gadepalli , Timo Kohlberger
Abstract: A method, system and machine for assisting a pathologist in identifying the presence of tumor cells in lymph node tissue is disclosed. The digital image of lymph node tissue at a first magnification (e.g., 40×) is subdivided into a multitude of rectangular “patches.” A likelihood of malignancy score is then determined for each of the patches. The score is obtained by analyzing pixel data from the patch (e.g., pixel data centered on and including the patch) using a computer system programmed as an ensemble of deep neural network pattern recognizers, each operating on different magnification levels of the patch. A representation or “heatmap” of the slide is generated. Each of the patches is assigned a color or grayscale value in accordance with (1) the likelihood of malignancy score assigned to the patch by the combined outputs of the ensemble of deep neural network pattern recognizers and (2) a code which assigns distinct colors (or grayscale values) to different values of likelihood of malignancy scores assigned to the patches.
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2.发明授权公开(公告)号：US11170897B2
公开(公告)日：2021-11-09
申请号：US16488029
申请日：2017-02-23
Applicant: Google LLC
Inventor： Martin Christian Stumpe , Lily Peng , Yun Liu , Krishna K. Gadepalli , Timo Kohlberger
Abstract: A method, system and machine for assisting a pathologist in identifying the presence of tumor cells in lymph node tissue is disclosed. The digital image of lymph node tissue at a first magnification (e.g., 40×) is subdivided into a multitude of rectangular “patches.” A likelihood of malignancy score is then determined for each of the patches. The score is obtained by analyzing pixel data from the patch (e.g., pixel data centered on and including the patch) using a computer system programmed as an ensemble of deep neural network pattern recognizers, each operating on different magnification levels of the patch. A representation or “heatmap” of the slide is generated. Each of the patches is assigned a color or grayscale value in accordance with (1) the likelihood of malignancy score assigned to the patch by the combined outputs of the ensemble of deep neural network pattern recognizers and (2) a code which assigns distinct colors (or grayscale values) to different values of likelihood of malignancy scores assigned to the patches.
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公开(公告)号：US11783604B2
公开(公告)日：2023-10-10
申请号：US16959725
申请日：2018-01-11
Applicant: GOOGLE LLC
Inventor： Lily Peng , Martin Stumpe
CPC classification number: G06V20/695 , G01N1/30 , G01N1/34 , G06T7/0012 , G06T2207/20081 , G06T2207/20084 , G06T2207/30024
Abstract: A method for generating a ground truth mask for a microscope slide having a tissue specimen placed thereon includes a step of staining the tissue specimen with hematoxylin and eosin (H&E) staining agents. A first magnified image of the H&E stained tissue specimen is obtained, e.g., with a whole slide scanner. The H&E staining agents are then washed from the tissue specimen. A second, different stain is applied to the tissue specimen, e.g., a special stain such as an IHC stain. A second magnified image of the tissue specimen stained with the second, different stain is obtained. The first and second magnified images are then registered to each other. An annotation (e.g., drawing operation) is then performed on either the first or the second magnified images so as to form a ground truth mask, the ground truth mask in the form of closed polygon region enclosing tumor cells present in either the first or second magnified image.
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公开(公告)号：US11379516B2
公开(公告)日：2022-07-05
申请号：US16978102
申请日：2018-03-29
Applicant: GOOGLE LLC
Inventor： Lily Peng , Martin Stumpe , Daniel Smilkov , Jason Hipp
IPC: G06F16/532 , G06F16/55 , G06F16/583 , G06F16/538 , G06F16/2457 , G16H30/20 , G16H50/20 , G06K9/62 , G06V10/75
Abstract: A system for searching for similar medical images includes a reference library in the form of a multitude of medical images, at least some of which are associated with metadata including clinical information relating to the specimen or patient associated with the medical images. A computer system is configured as a search tool for receiving an input image query from a user. The computer system is trained to find one or more similar medical images in the reference library system which are similar to the input image. The reference library is represented as an embedding of each of the medical images projected in a feature space having a plurality of axes, wherein the embedding is characterized by two aspects of a similarity ranking: (1) visual similarity, and (2) semantic similarity such that neighboring images in the feature space are visually similar and semantic information is represented by the axes of the feature space. The computer system supports additional queries from a user to thereby further refine a search for medical images similar to the input image within a search space consisting of the one or more similar medical images.
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