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    ANALYSIS OF HISTOPATHOLOGY SAMPLES
    2.
    发明公开
    ANALYSIS OF HISTOPATHOLOGY SAMPLES 审中-公开

    公开(公告)号：US20240233416A1

    公开(公告)日：2024-07-11

    申请号：US18289299

    申请日：2022-05-04

    Applicant: The Institute of Cancer Research: Royal Cancer Hospital

    Inventor： Hanyun Zhang ,  Yinyin Yuan

    IPC: G06V20/69 ,  G06V10/74 ,  G06V10/772 ,  G06V10/774 ,  G06V10/82

    CPC classification number: G06V20/698 ,  G06V10/761 ,  G06V10/772 ,  G06V10/774 ,  G06V10/82 ,  G06V2201/03

    Abstract: Methods and systems for analysing the cellular composition of a sample are described, comprising: providing an image of the sample in which a plurality of cellular populations are associated with respective signals and classifying a plurality of query cells in the image between a plurality of classes corresponding to respective cellular populations in the plurality of cellular populations. This is performed by providing a query single cell image to an encoder module of a machine learning model to produce a feature vector for the query image, and assigning the query cell to one of the plurality of classes based on the feature vector for the query image and feature vectors produced by the encoder module for each of a plurality of reference single cell images. The machine leaning model comprises: the encoder module, configured to take as input a single cell image and to produce as output a feature vector the single cell image, and a similarity module configured to take as input a pair of feature vectors for a pair of single cell images and to produce as output a score indicative of the similarity between the single cell images. Thus, the machine learning model can be obtained without the need for an extensively annotated dataset. The methods find use in the analysis of multiplex immunohistochemistry/immunofluorescence in a variety of clinical contexts.

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