公开(公告)号：US11816185B1

公开(公告)日：2023-11-14

申请号：US16383347

申请日：2019-04-12

Applicant: Nvidia Corporation

Inventor： Holger Roth ,  Yingda Xia ,  Dong Yang ,  Daguang Xu

IPC: G06F18/214 ,  G06F9/30 ,  G06N3/08 ,  G16H30/40 ,  G06N5/04 ,  G06F18/211 ,  G06F18/2433 ,  G06N3/045

CPC classification number: G06F18/2155 ,  G06F9/3001 ,  G06F18/211 ,  G06F18/2433 ,  G06N3/045 ,  G06N3/08 ,  G06N5/04 ,  G16H30/40 ,  G06V2201/031

Abstract: Volumetric quantification can be performed for various parameters of an object represented in volumetric data. Multiple views of the object can be generated, and those views provided to a set of neural networks that can generate inferences in parallel. The inferences from the different networks can be used to generate pseudo-labels for the data, for comparison purposes, which enables a co-training loss to be determined for the unlabeled data. The co-training loss can then be used to update the relevant network parameters for the overall data analysis network. If supervised data is also available then the network parameters can further be updated using the supervised loss.

公开(公告)号：US11813113B2

公开(公告)日：2023-11-14

申请号：US17205485

申请日：2021-03-18

Applicant: Merative US L.P.

Inventor： Ehsan Dehghan Marvast ,  Allen Lu ,  Tanveer F. Syeda-Mahmood

IPC: G06T7/00 ,  G06T7/62 ,  A61B8/08 ,  A61B5/00 ,  G16H30/40 ,  A61B5/318 ,  A61B5/316 ,  G06V10/44 ,  G06F18/21 ,  G06F18/25 ,  G06V10/80 ,  G16H10/60

CPC classification number: A61B8/0883 ,  A61B5/316 ,  A61B5/318 ,  A61B5/72 ,  A61B8/5223 ,  G06F18/21 ,  G06F18/253 ,  G06T7/0012 ,  G06T7/0014 ,  G06T7/62 ,  G06V10/454 ,  G06V10/806 ,  G16H30/40 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30048 ,  G06V2201/031 ,  G16H10/60

Abstract: Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.

公开(公告)号：US20230351593A1

公开(公告)日：2023-11-02

申请号：US18316010

申请日：2023-05-11

Applicant: EKO.AI PTE. LTD.

Inventor： James Otis HARE, II ,  Su Ping Carolyn LAM ,  Yoran HUMMEL ,  Matthew FROST ,  Mathias IVERSEN ,  Cyril EQUILBEC ,  Zhubo JIANG

IPC: G06T7/00 ,  G06T7/11 ,  G06T7/90 ,  G06V20/40 ,  G06V10/764 ,  G06V10/82 ,  G16H30/40 ,  G16H70/20

CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06T7/90 ,  G06V20/49 ,  G06V10/764 ,  G06V10/82 ,  G16H30/40 ,  G16H70/20 ,  G06T2207/30048 ,  G06T2207/10016 ,  G06V2201/031 ,  G06T2207/20084 ,  G06T2207/20081 ,  G06T2207/10132 ,  G06T2207/30101

Abstract: A computer-implemented method for grading of Aortic Stenosis severity performed by an automated workflow engine executed by at least one processor includes receiving, from a memory, a plurality of echocardiogram images a heart. The plurality of echocardiogram (echo) images according to 2D images and Doppler modality images. The 2D images are classified by view type, and the Doppler modality images are classified by region. The regions of interest in the 2D images are segmented to produce segmented 2D images. The Doppler modality images are segmented to generate waveform traces to produce segmented Doppler modality images. The segmented images are used to calculate measurements of cardiac features of the heart. A conclusion of Aortic Stenosis severity is generated by comparing the calculated measurements to cardiac guidelines. A report is then output showing the calculated measurements of the cardiac features that fall within or outside of the cardiac guidelines.

公开(公告)号：US20230317254A1

公开(公告)日：2023-10-05

申请号：US18332762

申请日：2023-06-12

Applicant: FUJIFILM Corporation

Inventor： Keigo Nakamura

IPC: G16H30/40 ,  G06V10/764 ,  G06T7/00

CPC classification number: G16H30/40 ,  G06V10/764 ,  G06T7/0012 ,  G06V2201/031

Abstract: A document creation support apparatus includes at least one processor. The processor generates text describing a classification of a disease for at least one feature portion included in an image, and includes, in the text, a description regarding a relevant portion related to the classification of the disease described in the text.

公开(公告)号：US11776115B2

公开(公告)日：2023-10-03

申请号：US16251730

申请日：2019-01-18

Applicant: Biocellvia

Inventor： Jean-Claude Gilhodes ,  Yvon Julé ,  Tomi Florent

IPC: G06T7/00 ,  G06T7/136 ,  G06T7/13 ,  G06T11/00 ,  G06T7/62 ,  G06V10/50 ,  G06V20/69 ,  G06F18/211 ,  G06F18/2431

CPC classification number: G06T7/0012 ,  G06F18/211 ,  G06F18/2431 ,  G06T7/13 ,  G06T7/136 ,  G06T7/62 ,  G06T11/001 ,  G06V10/50 ,  G06V20/695 ,  G06T2207/20072 ,  G06T2207/30061 ,  G06T2207/30101 ,  G06T2207/30242 ,  G06V2201/031

Abstract: A method for determining a quantity of interest related to the density of organic tissue starts with a digital representation of a histological image of the tissue. The digital representation is converted to a binary image, to discriminate pixels that represent tissue of interest in the image. A box filter is applied to values of the pixels of interest to obtain a tissue density value for each pixel of interest. A quantity of interest is computed, based upon the tissue density values for the pixels of interest. A tangible representation of the computed quantity of interest, such as a numerical value, a graph, or a color representation, is displayed or otherwise presented via an interface.

公开(公告)号：US11734826B2

公开(公告)日：2023-08-22

申请号：US17173259

申请日：2021-02-11

Applicant: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

Inventor： Si Hong Chen ,  Ye Feng Zheng

IPC: G06T7/11 ,  G06T7/73 ,  G06T7/246 ,  G06T3/00 ,  G06V20/40 ,  G06F18/213 ,  G06F18/214

CPC classification number: G06T7/11 ,  G06F18/213 ,  G06F18/2148 ,  G06T3/0006 ,  G06T7/246 ,  G06T7/74 ,  G06V20/40 ,  G06V20/41 ,  G06V20/46 ,  G06T2207/10016 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20216 ,  G06T2207/30048 ,  G06V2201/031

Abstract: An image segmentation method, apparatus, and a storage medium are provided. The method includes: selecting a current image frame in a video according to a time sequence of the video; determining a reference image frame before the current image frame in the time sequence; obtaining first location information of a target object key point in the reference image frame; performing an affine transformation on the current image frame with reference to an affine transformation relationship between the first location information and a target object key point template to obtain a target object diagram; performing a key point detection on the target object diagram to obtain second location information of the target object key point; segmenting a target object from the target object diagram to obtain segmentation information; and obtaining the target object from the current image frame according to the segmentation information and the second location information.

公开(公告)号：US20230154141A1

公开(公告)日：2023-05-18

申请号：US17916589

申请日：2021-03-23

Applicant: THE UNIVERSITY OF HONG KONG

Inventor： Philip Leung Ho Yu ,  Keith Chiu ,  Man Fung Yuen ,  Wai Kay Walter Seto

IPC: G06V10/44 ,  G06V10/82

CPC classification number: G06V10/454 ,  G06V10/82 ,  G06V2201/031

Abstract: Disclosed are systems and methods using artificial intelligence for the detection and characterization of liver cancers.

公开(公告)号：US20240307025A1

公开(公告)日：2024-09-19

申请号：US18602510

申请日：2024-03-12

Applicant: Konica Minolta, Inc.

Inventor： Hiroaki MATSUMOTO ,  Yoshihiro TAKEDA

IPC: A61B8/08 ,  G06T7/00 ,  G06T7/70 ,  G06V10/82 ,  G16H50/20

CPC classification number: A61B8/0883 ,  A61B8/5223 ,  G06T7/0012 ,  G06T7/70 ,  G06V10/82 ,  G16H50/20 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30048 ,  G06V2201/031 ,  G06V2201/07

Abstract: An image diagnostic technique using a machine learning model is disclosed. An aspect of the present disclosure relates to a machine learning model trained by using training data that includes first ultrasound image data based on a reception signal received by an ultrasound probe; first ground truth data that is first region information associated with a detection target of the first ultrasound image data; and second ground truth data that is first position information associated with the detection target of the first ultrasound image data or that is second region information based on the first position information.

公开(公告)号：US12067720B2

公开(公告)日：2024-08-20

申请号：US17718747

申请日：2022-04-12

Applicant: Ventana Medical Systems, Inc. ,  HOFFMANN-LA ROCHE INC.

Inventor： Joerg Bredno ,  Astrid Heller ,  Gabriele Hoelzlwimmer

IPC: G06K9/00 ,  G06F18/211 ,  G06T7/00 ,  G06T7/11 ,  G06V20/69 ,  G02B21/36 ,  G06F3/0354

CPC classification number: G06T7/0012 ,  G06F18/211 ,  G06T7/0014 ,  G06T7/11 ,  G06V20/69 ,  G06V20/695 ,  G02B21/365 ,  G06F3/03545 ,  G06T2200/24 ,  G06T2207/10056 ,  G06T2207/20012 ,  G06T2207/30024 ,  G06T2207/30096 ,  G06T2207/30168 ,  G06T2207/30204 ,  G06V2201/031 ,  G06V2201/07

Abstract: The subject disclosure presents systems and methods for automatically selecting meaningful regions on a whole-slide image and performing quality control on the resulting collection of FOVs. Density maps may be generated quantifying the local density of detection results. The heat maps as well as combinations of maps (such as a local sum, ratio, etc.) may be provided as input into an automated FOV selection operation. The selection operation may select regions of each heat map that represent extreme and average representative regions, based on one or more rules. One or more rules may be defined in order to generate the list of candidate FOVs. The rules may generally be formulated such that FOVs chosen for quality control are the ones that require the most scrutiny and will benefit the most from an assessment by an expert observer.

公开(公告)号：US12067717B2

公开(公告)日：2024-08-20

申请号：US17507948

申请日：2021-10-22

Applicant: National Taiwan University

Inventor： Wei-Chung Wang ,  Wei-Chih Liao ,  Kao-Lang Liu ,  Po-Ting Chen ,  Po-Chuan Wang ,  Ting-Hui Wu

IPC: G06T7/00 ,  A61B5/00 ,  G06N3/08 ,  G06T7/70 ,  G06T7/73 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82 ,  G16H30/20

CPC classification number: G06T7/0012 ,  A61B5/425 ,  A61B5/4887 ,  A61B5/726 ,  A61B5/7267 ,  G06N3/08 ,  G06T7/70 ,  G06T7/73 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82 ,  G16H30/20 ,  G06T2207/20016 ,  G06T2207/20064 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30096 ,  G06T2207/30204 ,  G06V2201/031

Abstract: A medical image analyzing system and a medical image analyzing method are provided and include inputting at least one patient image into a first model of a first neural network module to obtain a result having determined positions and ranges of an organ and a tumor of the patient image; inputting the result into a plurality of second models of a second neural network module, respectively, to obtain a plurality of prediction values corresponding to each of the plurality of second models and a model number predicting having cancer in the plurality of prediction values; and outputting a determined result based on the model number predicting having cancer and a number threshold value. Further, processes between the first model and the second models can be automated, thereby improving identification rate of pancreatic cancer.