公开(公告)号：WO2022002129A1

公开(公告)日：2022-01-06

申请号：PCT/CN2021/103541

申请日：2021-06-30

Applicant: 华为技术有限公司

Inventor： 戴同武

IPC: G06K9/00 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06K9/6268 ,  G06N3/0454 ,  G06V20/698

Abstract: 公开了一种识别物体的卫生状况方法及相关电子设备，涉及人工智能领域，与计算机视觉相关。包括：电子设备确定第一物体的种类；该电子设备通过第一摄像头采集该第一物体的第一图像，该第一图像为微观图像；该电子设备根据该第一物体的种类和该第一图像，获得该第一物体的卫生状况；其中电子设备根据第一物体的微观图像可以获取第一物体上存在的细菌的种类和数量等信息，也可以获取第一物体的色泽、纹理、气孔等信息。这样，电子设备能够结合物体的种类和物体的微观图像进行综合分析，确定出该物体的卫生状况，并输出智能提示。

公开(公告)号：WO2023288323A2

公开(公告)日：2023-01-19

申请号：PCT/US2022/073806

申请日：2022-07-15

Applicant: TECHCYTE, INC. ,  NANOSPOT.AL, INC.

Inventor： CAHOON, Benjamin ,  SMITH, Richard ,  SWENSON, Shane ,  WORTHEN, Bryan ,  ZIMMERMAN, Russ ,  REDECKE, Vanessa ,  HAECKER, Hans ,  ASTILL, Mark ,  WENDLING, Rian

IPC: G01N33/86 ,  G01N33/49 ,  G01N33/569 ,  G01N33/68 ,  G16B40/20 ,  G16H30/00 ,  G01N2021/0125 ,  G01N2021/0143 ,  G01N2021/0181 ,  G01N2021/825 ,  G01N21/82 ,  G01N2333/08 ,  G01N2333/165 ,  G01N33/48771 ,  G01N33/4905 ,  G01N33/5304 ,  G01N33/54306 ,  G01N33/54387 ,  G01N33/56983 ,  G01N33/80 ,  G06F18/24 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06T7/0012 ,  G06T7/0014 ,  G06V10/774 ,  G06V20/698 ,  G16H10/40 ,  G16H10/60 ,  G16H30/20 ,  G16H30/40 ,  G16H40/40 ,  G16H40/67 ,  G16H50/20 ,  G16H50/70 ,  G16H80/00

Abstract: Machine learning image analysis for quantitative and qualitative analysis of agglutination samples. A method includes receiving an image of an agglutination assay comprising a negative control sample, a positive control sample, and a test sample. The method includes providing the image to a machine learning algorithm trained to classify agglutination of the test sample on a quantitative scale. The machine learning algorithm calibrates the quantitative scale based at least in part on the negative control sample and the positive control sample.

公开(公告)号：WO2021260396A1

公开(公告)日：2021-12-30

申请号：PCT/GB2021/051630

申请日：2021-06-28

Applicant: OXFORD UNIVERSITY INNOVATION LIMITED ,  GROUND TRUTH LABS LTD

Inventor： ABERDEEN, Alan ,  ROYSTON, Daniel ,  THEISSEN, Helen ,  SIRINUKUNWATTANA, Korsuk ,  RITTSCHER, Jens

IPC: G06K9/00 ,  G16B40/20 ,  G16B40/30 ,  G16H10/40 ,  G16H50/20 ,  G16H50/70 ,  G06K9/62 ,  G06K9/6256 ,  G06V20/698

Abstract: Methods and systems for generating visual representations of variation of disease-relevant classification are disclosed. Training data is received that comprises sample data units from subjects that represent information about a biological sample via an N-dimensional set of values. A dimensionality reduction algorithm represents each sample data unit as a respective point in a reduced dimension parameter space. Distributions of points from the dimensionality reduction are used to derive a probability density distribution for each of a plurality of disease-relevant classifications in the reduced dimension parameter space. A visual representation of each of the derived probability density distributions in the reduced dimension parameter space is generated to provide a visual representation of disease-relevant classification variation over the parameter space.

公开(公告)号：WO2021260159A1

公开(公告)日：2021-12-30

申请号：PCT/EP2021/067438

申请日：2021-06-24

Applicant: UNIVERSITÉ DE LAUSANNE

Inventor： ÖZEL DUYGAN, Birge

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/6272 ,  G06V20/698

Abstract: The invention relates to the field of machine learning and comprises supervised learning. In particular, the invention relates to a computer-implemented method for generating a classifier for at least one target microbe by employing supervised machine learning, e.g., an artificial neural network, a classifier that is obtainable by said method, and applications of the inventive classifier. Thus, the invention further relates to a method for quantifying the abundance of at least one target microbe in a sample, and a method for analyzing the microbial composition in a sample. Further provided herein are diagnostic uses of the classifier, i.e. a method for diagnosing a microbial disease in a subject. In addition, the invention relates to a set of standards comprised in the classifier, a computer-readable storage medium, and/or a kit.

公开(公告)号：WO2023009870A1

公开(公告)日：2023-02-02

申请号：PCT/US2022/038960

申请日：2022-07-29

Applicant: TEMPUS LABS, INC.

Inventor： HO, Chi-Sing ,  KANNAN, Madhavi ,  KHARE, Sonal ,  LARSEN, Brian ,  MAPES, Brandon ,  SALAHUDEEN, Ameen ,  VENKATARAMAN, Jagadish

IPC: G01N33/487 ,  G06T7/10 ,  G06T7/174 ,  G01N2021/6439 ,  G01N21/6428 ,  G01N21/6456 ,  G01N33/5011 ,  G01N33/5082 ,  G06T2200/04 ,  G06T2207/10064 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06T2207/30072 ,  G06T3/0031 ,  G06T7/0016 ,  G06T7/11 ,  G06V10/774 ,  G06V20/695 ,  G06V20/698 ,  G16H15/00 ,  G16H30/40

Abstract: A method for characterizing cancer organoid response to an immune cell based therapy, includes providing a panel of different combinations of cancer organoid cells and immune cells to culturing wells and culturing the different combination under conditions that support organoid growth. Brightfield and corresponding fluorescence images of the culturing wells are captured and provided to one or more trained machine learning algorithms that identify and distinguish cancer organoid cells from immune cells and characterize cancer organoid morphology changes caused by an immune cell based therapies, from which an analytical report including a characterization of cancer organoid cell death caused by the immune cell based therapy is provided.

公开(公告)号：WO2022238232A1

公开(公告)日：2022-11-17

申请号：PCT/EP2022/062162

申请日：2022-05-05

Applicant: SHELL INTERNATIONALE RESEARCH MAATSCHAPPIJ B.V. ,  SHELL USA, INC.

Inventor： FALIVENE ALDEA, Oriol ,  KLEIPOOL, Lucas Maarten ,  AUCHTER, Neal Christian

IPC: G06V10/25 ,  G06V10/82 ,  G06V20/69 ,  G06V20/698

Abstract: A method for predicting an occurrence of a geological feature in a geologic thin section image uses a backpropagation-enabled classification process trained by inputting extracted training image fractions having substantially the same absolute horizontal and vertical length and associated labels for classes from a predetermined set of geological features, and iteratively computing a prediction of the probability of occurrence of each of the classes for the extracted training image fractions. The trained backpropagation-enabled classification model is used to predict the occurrence of the classes in extracted fractions of non-training geologic thin section images having substantially the same absolute horizontal and vertical length as the training image fractions.

公开(公告)号：WO2022053685A2

公开(公告)日：2022-03-17

申请号：PCT/EP2021/075116

申请日：2021-09-13

Applicant: MEDIMMUNE LIMITED

Inventor： SCHMIDT, Guenter ,  BRIEU, Nicolas ,  SPITZMUELLER, Andreas ,  KAPIL, Ansh ,  TAN, Tze, Heng ,  WORTMANN, Philipp

IPC: G06K9/00 ,  C07K16/28 ,  C07K16/30 ,  G06K9/62 ,  C07K16/2827 ,  G06K9/6215 ,  G06K9/6271 ,  G06K9/6292 ,  G06V10/454 ,  G06V20/695 ,  G06V20/698

Abstract: The present invention relates to a method for predicting how a cancer patient will respond to an antibody drug conjugate (ADC) therapy involving computing a predictive response score based on single-cell ADC scores for each cancer cell. For each cancer cell, a single-cell ADC score is computed based on the staining intensities of the dye in the membrane and cytoplasm of the cancer cell and in the membranes and cytoplasms of neighboring cancer cells. The present invention also relates to predicting a response of a cancer patient to ADC therapy by aggregating all single-cell ADC scores of the tissue sample using a statistical operation, and the subsequent treatment of cancer with related antibody-drug conjugates.

公开(公告)号：WO2021247868A1

公开(公告)日：2021-12-09

申请号：PCT/US2021/035707

申请日：2021-06-03

Applicant: CASE WESTERN RESERVE UNIVERSITY

Inventor： PRALJAK, Niksa ,  IRAM, Shamreen ,  GOREKE, Utku ,  SINGH, Gundeep ,  HILL, Ailis ,  GURKAN, Umut ,  HINCZEWSKI, Michael

IPC: G01N15/14 ,  G01N15/10 ,  G01N33/49 ,  G06K9/00 ,  G06K9/62 ,  G06T7/00 ,  G06V10/454 ,  G06V10/82 ,  G06V20/695 ,  G06V20/698

Abstract: In a disclosed example, a computer-implemented method includes storing image data that includes an input image of a blood sample within a blood monitoring device. The method also includes generating, by a machine learning model, a segmentation mask that assigns pixels in the input image to one of a plurality of classes, which correlate to respective known biophysical properties of blood cells. The method also includes extracting cell images from the input image based on the segmentation mask, in which each extracted cell image includes a respective cluster of the pixels assigned to a respective one of the plurality of classes.

公开(公告)号：WO2021239533A1

公开(公告)日：2021-12-02

申请号：PCT/EP2021/063258

申请日：2021-05-19

Applicant: SARTORIUS STEDIM DATA ANALYTICS AB

Inventor： SJÖGREN, Rickard ,  EDLUND, Christoffer ,  SEHLSTEDT, Mattias

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/6267 ,  G06V10/82 ,  G06V20/48 ,  G06V20/49 ,  G06V20/695 ,  G06V20/698

Abstract: A computer-implemented method is provided for analyzing videos of a living system captured with microscopic imaging. The method comprises: obtaining (S10) a base dataset including one or more videos captured with microscopic imaging, at least one of the one or more videos including a cellular event; cropping out (S30), from the base dataset, sub-videos including one or more objects of interest that may be involved in the cellular event; receiving (S40) information indicating a plurality of sub-videos selected from among the sub-videos that are cropped out from the base dataset, the plurality of selected sub-videos including the cellular event; training (S50) an artificial neural network, ANN, model, using the plurality of selected sub-videos as training data, to perform unsupervised video alignment; obtaining (S602) a query sub-video, the query sub-video being: one of the sub-videos that are cropped out from the base dataset, or a sub-video cropped out from a video that is captured with microscopic imaging and that is not included in the base dataset; aligning (S604), using the trained ANN model, the query sub-video with a reference sub-video that is one of the plurality of selected sub-videos; and determining (S606), according to a result of the aligning, whether or not the query sub-video includes the cellular event.

公开(公告)号：WO2022269360A1

公开(公告)日：2022-12-29

申请号：PCT/IB2022/000369

申请日：2022-06-24

Applicant: BIO-RAD EUROPE GMBH

Inventor： PICARD, Thomas

IPC: G06T7/00 ,  G06V10/82 ,  G06V20/69 ,  G06V10/98 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06T7/0014 ,  G06V10/993 ,  G06V20/698

Abstract: An input image is received from testing equipment. One or more synthetic images are generated by applying an image-to-image translation model to the input image. Based on the one or more synthetic images, a binary classifier is applied to determine a classification for the received input image.