公开(公告)号：US20240029387A1

公开(公告)日：2024-01-25

申请号：US18023973

申请日：2021-08-16

Applicant: ANKON TECHNOLOGIES CO., LTD

Inventor： Tingqi WANG ,  Fei GAO ,  Xiaodong DUAN ,  Xiaohua HOU ,  Xiaoping XIE ,  Xuelian XIANG

IPC: G06V10/10 ,  G06V10/82 ,  G06V10/764 ,  G06V10/77 ,  G06V10/26

CPC classification number: G06V10/16 ,  G06V10/82 ,  G06V10/764 ,  G06V10/7715 ,  G06V10/26 ,  G06V2201/03

Abstract: The present invention provides an image recognition method, an electronic device and a computer-readable storage medium. The method includes: segmenting an original image into a plurality of unit images having the same predetermined size; inputting the unit images into a pre-built neural network model to carry out processing, so as to correspondingly add a detection frame to a marker in each unit image to form a pre-detection unit image; stitching a plurality of pre-detection unit images into a pre-output image according to segmentation positions of each unit image in the original image; determining whether the markers selected in two adjacent detection frames in the pre-output image are the same marker; outputting the image with the detection frames until all the detection frames confirmed to have the same markers are all merged. The present invention can effectively recognize types and positions of the markers in the image.

公开(公告)号：US20240020833A1

公开(公告)日：2024-01-18

申请号：US18340306

申请日：2023-06-23

Applicant: UT-Battelle, LLC

Inventor： Olga S. Ovchinnikova ,  Jacob D. Hinkle ,  Inzamam Haque ,  Debangshu Mukherjee

IPC: G06T7/00 ,  G06T3/40 ,  G06V10/77 ,  G06V10/774 ,  G06V20/69 ,  G16H30/40

CPC classification number: G06T7/0012 ,  G06T3/40 ,  G06V10/7715 ,  G06V10/774 ,  G06V20/698 ,  G16H30/40 ,  G06T2207/30096 ,  G06T2207/20081 ,  G06T2207/30204 ,  G06T2207/30088 ,  G06T2207/30081 ,  G06T2200/24 ,  G06T2207/20016 ,  G06T2207/10056 ,  G06V2201/03

Abstract: Systems, methods and programs including machine learning techniques which can predict a spectral image directly from an optical image of a patient's tissue sample using a first model. There may be different first models for different cancers. Each first model may be trained by using a plurality of pairs of images from different samples, respectively, where each image in a respective pair may be obtained via different imaging modalities. The systems, methods and programs may also include machine learning techniques which can predict cancer labels from the predicted spectral image using a second model. There may be different second models for different cancers. Each second model may be trained using multiple spectral images and corresponding manually input cancer labels.

公开(公告)号：US20240020828A1

公开(公告)日：2024-01-18

申请号：US18034713

申请日：2021-12-30

Applicant: CHECK-CAP LTD.

Inventor： Yoav KIMCHY

IPC: G06T7/00 ,  A61B6/00 ,  G16H10/60 ,  G16H50/20 ,  G06V10/764 ,  G06V10/774

CPC classification number: G06T7/0012 ,  A61B6/4057 ,  A61B6/485 ,  G16H10/60 ,  G16H50/20 ,  G06V10/764 ,  G06V10/774 ,  G06T2207/20076 ,  G06T2207/10116 ,  G06T2207/30092 ,  G06T2207/20084 ,  G06T2207/20081 ,  G06V2201/03

Abstract: A system for gastrointestinal examination, including an imaging capsule configured to scan the gastrointestinal tract of a patient using radiation and configured to measure X-ray fluorescence and Compton backscattering and transmit the measurements, a computer with a processor and memory configured to receive the measurements from the imaging capsule, a trained machine learning module executed by the computer configured to analyze the measurements and provide a probability score representing the probability of the existence of a polyp or other abnormalities in the gastrointestinal tract of the patient.

公开(公告)号：US20240005502A1

公开(公告)日：2024-01-04

申请号：US18255432

申请日：2021-11-30

Applicant: Johnson & Johnson Enterprise Innovation Inc.

Inventor： George R. Washko, JR. ,  Raul San Jose Estepar ,  Charles Matthew Kinsey ,  Christopher Scott Stevenson

IPC: G06T7/00 ,  G06V10/774 ,  G06V10/77 ,  G06V10/764 ,  G16H50/30 ,  G16H30/40 ,  G16H50/20

CPC classification number: G06T7/0014 ,  G06V10/774 ,  G06V10/7715 ,  G06V10/764 ,  G16H50/30 ,  G16H30/40 ,  G16H50/20 ,  G06T2207/20081 ,  G06T2207/30096 ,  G06T2207/10081 ,  G06T2207/10068 ,  G06T2207/10104 ,  G06T2207/10116 ,  G06T2207/10088 ,  G06T2207/10132 ,  G06T2207/20064 ,  G06T2207/30064 ,  G06V2201/03 ,  G06T2207/20084

Abstract: Disclosed herein are methods for determining a subject level risk of metastatic cancer involving the training and/or deployment of models to determine 1) a lymph node level risk of individual lymph node involvement and/or 2) a subject level risk of lymph node involvement. Thus, the methods can identify patients who are high or low risk for having nodal disease and optionally enable the guided intervention of cancer patients, for example, via treatment.

公开(公告)号：US20240005501A1

公开(公告)日：2024-01-04

申请号：US18252698

申请日：2021-11-11

Applicant: ASTRAZENECA AB

Inventor： Yinhai WANG ,  Adrian Mark FREEMAN

IPC: G06T7/00 ,  A61B5/00 ,  G06V10/764 ,  G06T7/55 ,  G06T7/60

CPC classification number: G06T7/0014 ,  A61B5/445 ,  G06V10/764 ,  G06T7/55 ,  G06T2207/20084 ,  G06T2207/10101 ,  G06V2201/03 ,  G06T2207/20081 ,  G06T2207/30088 ,  G06T7/60

Abstract: A method of assessing a wound in a subject is provided. The method comprises obtaining one or more optical coherence tomography images of the wound and analysing the one or more optical coherence tomography images using a deep learning model that has been trained to classify pixels in an optical coherence tomography image of a wound between a plurality of classes comprising a plurality of classes associated with different types of wound tissue, thereby obtaining for each image analysed, an indication of the location of tissue likely to belong to each of the different types of wound tissue in the respective image.

公开(公告)号：US11860154B2

公开(公告)日：2024-01-02

申请号：US17331893

申请日：2021-05-27

Applicant: Leuko Labs, Inc.

Inventor： Carlos Castro Gonzalez ,  Ian Butterworth ,  Aurelien Bourquard ,  Alvaro Sanchez Ferro

IPC: G01N33/49 ,  G06T7/00 ,  G06T7/11 ,  G06T7/38 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/70 ,  G01N21/31 ,  G06V20/69 ,  G06F18/214 ,  G06V10/764 ,  G06V10/82 ,  G06V10/56 ,  G01N15/14 ,  G01N15/00 ,  G01N15/10

CPC classification number: G01N33/49 ,  G01N15/147 ,  G01N15/1429 ,  G01N15/1475 ,  G01N21/31 ,  G06F18/2148 ,  G06T7/0016 ,  G06T7/11 ,  G06T7/38 ,  G06V10/56 ,  G06V10/764 ,  G06V10/82 ,  G06V20/695 ,  G06V20/698 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/70 ,  G01N2015/008 ,  G01N2015/0073 ,  G01N2015/1006 ,  G01N2015/1443 ,  G01N2015/1486 ,  G06T2207/10016 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30101 ,  G06T2207/30242 ,  G06V2201/03

Abstract: In one aspect, a method to detect white blood cells and/or white blood cell subtypes from non-invasive capillary videos is featured. The method includes acquiring a first plurality of images of a region of interest including one or more capillaries of a predetermined area of a human subject from non-invasive capillary videos captured with an optical device, processing the first plurality of images to determine one or more optical absorption gaps located in said capillary, and annotating the first plurality of images with an indication of any optical absorption gap detected in the first plurality of images. The method also includes acquiring a second plurality of images of the same region of interest of the same capillary with an advanced optical device capable of resolving cellular structure of white blood cells and white blood cell subtypes and spatiotemporally annotating the second plurality of images with an indication of any white blood cell detected and/or a subtype of any white blood cell detected in the second plurality of images. The method also includes inputting the first plurality of images and annotated information from the first plurality of images and annotated information from the spatiotemporally annotated second plurality of images into a machine learning subsystem configured to determine a presence of white blood cells and/or the subtype of any white blood cells present in the one or more optical absorption gaps in the first plurality of images.

公开(公告)号：US20230419694A1

公开(公告)日：2023-12-28

申请号：US18462930

申请日：2023-09-07

Applicant: Verily Life Sciences LLC

Inventor： Martin Stumpe ,  Philip Nelson ,  Lily Peng

IPC: G06V20/69 ,  G16H30/40 ,  G01N1/30 ,  G06N3/08 ,  G06T7/00 ,  G06T11/00 ,  G06F18/214 ,  G06V10/82

CPC classification number: G06V20/69 ,  G16H30/40 ,  G01N1/30 ,  G06N3/08 ,  G06T7/0012 ,  G06T11/001 ,  G06F18/214 ,  G06V10/82 ,  G06V20/695 ,  G01N2001/302 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06T2210/41 ,  G06V2201/03

Abstract: A machine learning predictor model is trained to generate a prediction of the appearance of a tissue sample stained with a special stain such as an IHC stain from an input image that is either unstained or stained with H&E. Training data takes the form of thousands of pairs of precisely aligned images, one of which is an image of a tissue specimen stained with H&E or unstained, and the other of which is an image of the tissue specimen stained with the special stain. The model can be trained to predict special stain images for a multitude of different tissue types and special stain types, in use, an input image, e.g., an H&E image of a given tissue specimen at a particular magnification level is provided to the model and the model generates a prediction of the appearance of the tissue specimen as if it were stained with the special stain. The predicted image is provided to a user and displayed, e.g., on a pathology workstation.

公开(公告)号：US11854196B2

公开(公告)日：2023-12-26

申请号：US17504414

申请日：2021-10-18

Applicant: Ventana Medical Systems, Inc.

Inventor： Yu-Heng Cheng ,  Setareh Duquette ,  Lisa A. Jones ,  Chih-Ching Lin ,  Javier Andres Perez-Sepulveda

IPC: G06V10/25 ,  G06T7/00 ,  G01N21/78 ,  G06V10/56 ,  G06V10/147 ,  G06V20/69 ,  G06T7/11 ,  G01N1/31 ,  G01N1/30

CPC classification number: G06T7/0012 ,  G01N1/312 ,  G01N21/78 ,  G06T7/11 ,  G06V10/147 ,  G06V10/25 ,  G06V10/56 ,  G06V20/695 ,  G01N1/30 ,  G06T2207/10024 ,  G06T2207/30024 ,  G06T2207/30072 ,  G06V2201/03

Abstract: A real time assay monitoring system and method can be used to monitor reagent volume in assays for fluid replenishment control, monitor assays in real-time to obtain quality control information, monitor assays in real-time during development to detect saturation levels that can be used to shorten assay time, and provide assay results before the assay is complete, enabling reflex testing to begin automatically. The monitoring system can include a real time imaging system with a camera and lights to capture images of the assay. The captured images can then be used to monitor and control the quality of the staining process in an assay, provide early assay results, and/or to measure the on-site reagent volume within the assay.

公开(公告)号：US11854194B2

公开(公告)日：2023-12-26

申请号：US17375876

申请日：2021-07-14

Applicant: Lunit Inc.

Inventor： Minje Jang

IPC: G06F18/214 ,  G06V10/82 ,  G06T7/00 ,  G06V10/426 ,  G06V20/69 ,  G06F18/25 ,  G06V10/764 ,  G06V10/774 ,  G06V10/80

CPC classification number: G06T7/0012 ,  G06F18/214 ,  G06F18/25 ,  G06V10/426 ,  G06V10/764 ,  G06V10/774 ,  G06V10/80 ,  G06V10/82 ,  G06V20/69 ,  G06T2207/20072 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06V2201/03

Abstract: An image analysis method and an image analysis system are disclosed. The method may include extracting training raw graphic data including at least one first node corresponding to a plurality of histological features of a training tissue slide image, and at least one first edge defined by a relationship between the histological features and generating training graphic data by sampling the first node of the training raw graphic data. The method may also include determining a parameter of a readout function by training a graph neural network (GNN) using the training graphic data and training output data corresponding to the training graphic data, and extracting inference graphic data including at least one second node corresponding to a plurality of histological features of an inference tissue slide image, and at least one second edge decided by a relationship between the histological features of the inference tissue slide image.

公开(公告)号：US11850021B2

公开(公告)日：2023-12-26

申请号：US17847796

申请日：2022-06-23

Applicant: HOLOGIC, INC.

Inventor： Haili Chui ,  Zhenxue Jing

IPC: G06K9/62 ,  A61B5/00 ,  G06N3/04 ,  G06N3/08 ,  G06F18/40 ,  G06F18/214 ,  G06F18/21 ,  G06V10/774 ,  G06V10/82

CPC classification number: A61B5/0033 ,  G06F18/214 ,  G06F18/217 ,  G06F18/40 ,  G06N3/04 ,  G06N3/08 ,  G06V10/774 ,  G06V10/82 ,  G06V2201/03

Abstract: A method and system for creating a dynamic self-learning medical image network system, wherein the method includes receiving, from a first node initial user interaction data pertaining to one or more user interactions with the one or more initially obtained medical images; training a deep learning algorithm based at least in part on the initial user interaction data received from the node; and transmitting an instance of the trained deep learning algorithm to the first node and/or to one or more additional nodes, wherein at each respective node to which the instance of the trained deep learning algorithm is transmitted, the trained deep learning algorithm is applied to respective one or more subsequently obtained medical images in order to obtain a result.