公开(公告)号：US20230245430A1

公开(公告)日：2023-08-03

申请号：US18161752

申请日：2023-01-30

Applicant: PAIGE.AI, INC.

Inventor： Hamed AGHDAM ,  Christopher KANAN

IPC: G06V10/774 ,  G06V10/764 ,  G06T7/00 ,  G06T7/11 ,  G06V20/70 ,  G16H30/40

CPC classification number: G06V10/774 ,  G06V10/764 ,  G06T7/0012 ,  G06T7/11 ,  G06V20/70 ,  G16H30/40 ,  G06T2207/30081 ,  G06T2207/30024 ,  G06T2207/20081 ,  G06V2201/03

Abstract: Systems and methods are described herein for processing electronic medical images to determine a first machine learning system, the first machine learning system having been trained to identify regions of electronic medical images; receive a plurality of electronic medical images, each of the electronic medical images being associated with one or more subcategories; determine a subset of the plurality of electronic medical images that are associated with only one subcategory of the one or more subcategories; provide the subset of the plurality of electronic medical images to the first machine learning system, the first machine learning system identifying regions within the subset of the plurality of electronic medical images associated with the subcategory; and train a second machine learning system, using the identified regions and the subset of the plurality of electronic medical images.

公开(公告)号：US20230230364A1

公开(公告)日：2023-07-20

申请号：US17926909

申请日：2020-05-26

Applicant: NEC Corporation

Inventor： Masahiro SAIKOU

IPC: G06V10/776 ,  G06T11/20 ,  G06T7/00 ,  A61B1/00

CPC classification number: G06V10/776 ,  G06T11/203 ,  G06T7/0012 ,  A61B1/0005 ,  A61B1/000094 ,  G06T2207/20081 ,  G06T2207/10068 ,  G06T2207/30096 ,  G06V2201/03

Abstract: The image processing device 1X includes a detection model evaluation means 31X and a display control means 33X. The detection model evaluation means 31X is configured to perform an evaluation on suitability of a detection model for detecting an attention area to be noted based on a captured image Ic in which an inspection target is photographed by a photographing unit provided in an endoscope. The display control means 33X is configured to display candidate area information according to a display mode determined based on a result of the evaluation, the candidate area information indicating one or more candidate areas that are one or more candidates of the attention area, the candidate areas being detected by one or more detection models included in detection model(s) subjected to the evaluation, the candidate area information being superimposed on the captured image Ic which is displayed by a display device 2X.

公开(公告)号：US20230230228A1

公开(公告)日：2023-07-20

申请号：US17648135

申请日：2022-01-17

Applicant: Siemens Healthcare GmbH

Inventor： Jingya Liu ,  Bin Lou ,  Ali Kamen

IPC: G06T7/00 ,  G06V10/82 ,  G06V10/74 ,  G06V10/40 ,  G16H30/40 ,  G16H50/20

CPC classification number: G06T7/0012 ,  G06V10/82 ,  G06V10/761 ,  G06V10/40 ,  G16H30/40 ,  G16H50/20 ,  G06T2207/20084 ,  G06T2207/20081 ,  G06T2207/30081 ,  G06T2207/30096 ,  G06V2201/03

Abstract: Systems and methods for determining whether input medical images are out-of-distribution of training images on which a machine learning based medical imaging analysis network is trained are provided. One or more input medical images of a patient are received. One or more reconstructed images of the one or more input medical images are generated using a machine learning based reconstruction network. It is determined whether the one or more input medical images are out-of-distribution from training images on which a machine learning based medical imaging analysis network is trained based on the one or more input medical images and the one or more reconstructed images. The determination of whether the one or more input medical images are out-of-distribution from the training images is output.

公开(公告)号：US20230215052A1

公开(公告)日：2023-07-06

申请号：US17849474

申请日：2022-06-24

Applicant: Kenneth Tang ,  Val Anthony Alvero ,  Yixiao Zhao

Inventor： Kenneth Tang ,  Val Anthony Alvero ,  Yixiao Zhao

IPC: G06T9/00 ,  G06V10/26 ,  G06V20/69 ,  G06T7/00

CPC classification number: G06T9/00 ,  G06V10/26 ,  G06V20/695 ,  G06T7/0012 ,  G06V2201/03 ,  G06T2207/20016 ,  G06T2207/30024

Abstract: Systems and methods for compressing images that include a memory storing an executable code and a processor executing the code to receive a whole slide image, the whole slide image containing a plurality of image layers and metadata associated with each image layer, extract a high-resolution image layer and the corresponding metadata, wherein the high-resolution image layer includes a plurality of image tiles including informative tiles and noninformative tiles, where the informative tiles depict a region of interest of the specimen, analyze the image tiles of the extracted high-resolution image layer, determine a first tile is a noninformative tile, create an informative image layer by removing the first tile from the extracted high-resolution image layer, the informative image layer containing a plurality of informative tiles, compress the informative image layer into a single-layer whole slide image, and save the single-layer whole slide image in the memory.

公开(公告)号：US20230210626A1

公开(公告)日：2023-07-06

申请号：US17922010

申请日：2021-04-07

Applicant: Wolfgang AUF

Inventor： Wolfgang AUF

IPC: A61B90/00 ,  A61B90/94 ,  A61B90/30 ,  A61B50/33 ,  H04N23/56 ,  G06V20/50 ,  G06V10/764 ,  G06V10/75 ,  H04N5/262 ,  H04N23/695 ,  G06T7/00 ,  G16H40/20

CPC classification number: A61B90/08 ,  A61B90/94 ,  A61B90/361 ,  A61B90/30 ,  A61B50/33 ,  H04N23/56 ,  G06V20/50 ,  G06V10/764 ,  G06V10/751 ,  H04N5/2628 ,  H04N23/695 ,  G06T7/0014 ,  G16H40/20 ,  A61B2090/0805 ,  G06V2201/03 ,  G06T2207/30052 ,  G06T2207/10016

Abstract: A method and device for documenting use of at least one implant used in a surgery and/or for the localization thereof. The implant can be provided for a surgery and used in the surgery. The method includes: a) providing a surgical set having a plurality of implants; b) capturing a first sequence of images of the plurality of implants of the surgical set using a device; c) analyzing the sequence of images of the plurality of implants in order to identify each individual implant; d) optionally outputting a signal when one and/or each implant has been identified; e) capturing a second sequence of images of the plurality of implants of the surgical set using the device after a surgery in order to ascertain missing implants; f) classifying a missing implant as used in surgery.

公开(公告)号：US11694137B2

公开(公告)日：2023-07-04

申请号：US17656526

申请日：2022-03-25

Applicant: Enlitic, Inc.

Inventor： Li Yao ,  Jordan Prosky ,  Eric C. Poblenz ,  Kevin Lyman ,  Ben Covington ,  Anthony Upton

IPC: G06Q10/0631 ,  G16H10/60 ,  G16H30/40 ,  G16H15/00 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00 ,  G06T11/00 ,  G06N5/04 ,  G16H30/20 ,  G06N20/00 ,  G06F9/54 ,  G06T7/187 ,  G06T7/11 ,  G06F3/0482 ,  G06T3/40 ,  A61B5/00 ,  G16H50/20 ,  G06F21/62 ,  G06Q20/14 ,  G16H40/20 ,  G06F3/0484 ,  G16H10/20 ,  G06N5/045 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  H04L67/12 ,  H04L67/01 ,  G06V10/82 ,  G06F18/40 ,  G06F18/214 ,  G06F18/21 ,  G06F18/2115 ,  G06F18/2415 ,  G06V10/25 ,  G06V30/19 ,  G06V10/764 ,  G06V40/16 ,  G06V10/22 ,  G16H50/70 ,  G06T7/70 ,  G16H50/30 ,  A61B5/055 ,  A61B6/03 ,  A61B8/00 ,  A61B6/00 ,  G06Q50/22 ,  G06F40/295 ,  G06F18/24 ,  G06F18/2111 ,  G06V30/194

CPC classification number: G06Q10/06315 ,  A61B5/7264 ,  G06F3/0482 ,  G06F3/0484 ,  G06F9/542 ,  G06F16/245 ,  G06F18/214 ,  G06F18/217 ,  G06F18/2115 ,  G06F18/2415 ,  G06F18/41 ,  G06F21/6254 ,  G06N5/04 ,  G06N5/045 ,  G06N20/00 ,  G06N20/20 ,  G06Q20/14 ,  G06T3/40 ,  G06T5/002 ,  G06T5/008 ,  G06T5/50 ,  G06T7/0012 ,  G06T7/0014 ,  G06T7/10 ,  G06T7/11 ,  G06T7/187 ,  G06T7/44 ,  G06T7/97 ,  G06T11/001 ,  G06T11/006 ,  G06T11/206 ,  G06V10/225 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  G06V30/19173 ,  G06V40/171 ,  G16H10/20 ,  G16H10/60 ,  G16H15/00 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H50/20 ,  H04L67/01 ,  H04L67/12 ,  A61B5/055 ,  A61B6/032 ,  A61B6/5217 ,  A61B8/4416 ,  G06F18/2111 ,  G06F18/24 ,  G06F40/295 ,  G06Q50/22 ,  G06T7/70 ,  G06T2200/24 ,  G06T2207/10048 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10116 ,  G06T2207/10132 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06T2207/30008 ,  G06T2207/30016 ,  G06T2207/30061 ,  G06V30/194 ,  G06V2201/03 ,  G16H50/30 ,  G16H50/70

Abstract: A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

公开(公告)号：US11694136B2

公开(公告)日：2023-07-04

申请号：US17656337

申请日：2022-03-24

Applicant: Enlitic, Inc.

Inventor： Kevin Lyman ,  Anthony Upton ,  Ben Covington ,  Li Yao ,  Keith Lui

IPC: G06Q10/0631 ,  G16H10/60 ,  G16H30/40 ,  G16H15/00 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00 ,  G06T11/00 ,  G06N5/04 ,  G16H30/20 ,  G06N20/00 ,  G06F9/54 ,  G06T7/187 ,  G06T7/11 ,  G06F3/0482 ,  G06T3/40 ,  A61B5/00 ,  G16H50/20 ,  G06F21/62 ,  G06Q20/14 ,  G16H40/20 ,  G06F3/0484 ,  G16H10/20 ,  G06N5/045 ,  G06T7/10 ,  G06T11/20 ,  G06F16/245 ,  G06T7/44 ,  G06N20/20 ,  H04L67/12 ,  H04L67/01 ,  G06V10/82 ,  G06F18/40 ,  G06F18/214 ,  G06F18/21 ,  G06F18/2115 ,  G06F18/2415 ,  G06V10/25 ,  G06V30/19 ,  G06V10/764 ,  G06V40/16 ,  G06V10/22 ,  G16H50/70 ,  G06T7/70 ,  G16H50/30 ,  A61B5/055 ,  A61B6/03 ,  A61B8/00 ,  A61B6/00 ,  G06Q50/22 ,  G06F40/295 ,  G06F18/24 ,  G06F18/2111 ,  G06V30/194

CPC classification number: G06Q10/06315 ,  A61B5/7264 ,  G06F3/0482 ,  G06F3/0484 ,  G06F9/542 ,  G06F16/245 ,  G06F18/214 ,  G06F18/217 ,  G06F18/2115 ,  G06F18/2415 ,  G06F18/41 ,  G06F21/6254 ,  G06N5/04 ,  G06N5/045 ,  G06N20/00 ,  G06N20/20 ,  G06Q20/14 ,  G06T3/40 ,  G06T5/002 ,  G06T5/008 ,  G06T5/50 ,  G06T7/0012 ,  G06T7/0014 ,  G06T7/10 ,  G06T7/11 ,  G06T7/187 ,  G06T7/44 ,  G06T7/97 ,  G06T11/001 ,  G06T11/006 ,  G06T11/206 ,  G06V10/225 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  G06V30/19173 ,  G06V40/171 ,  G16H10/20 ,  G16H10/60 ,  G16H15/00 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G16H50/20 ,  H04L67/01 ,  H04L67/12 ,  A61B5/055 ,  A61B6/032 ,  A61B6/5217 ,  A61B8/4416 ,  G06F18/2111 ,  G06F18/24 ,  G06F40/295 ,  G06Q50/22 ,  G06T7/70 ,  G06T2200/24 ,  G06T2207/10048 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10116 ,  G06T2207/10132 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06T2207/30008 ,  G06T2207/30016 ,  G06T2207/30061 ,  G06V30/194 ,  G06V2201/03 ,  G16H50/30 ,  G16H50/70

Abstract: A method includes generating a longitudinal lesion model by performing a training step on a plurality of sets of longitudinal data. Dates of medical scans of different ones of the plurality of sets of longitudinal data have relative time differences corresponding to different time spans, and each set of the plurality of sets of longitudinal data corresponds to one of a plurality of different patients. The longitudinal lesion model is utilized to perform an inference step on a received medical scan to generate, for a lesion detected in the received medical scan, a plurality of lesion change prediction data for a corresponding plurality of different projected time spans ending after the current date. At least one of the plurality of lesion change prediction data is transmitted for display.

公开(公告)号：US11690358B2

公开(公告)日：2023-07-04

申请号：US17429952

申请日：2020-12-06

Applicant: The State of Israel, Ministry of Agriculture & Rural Development, Agriculture Research Organization ,  ESHET EILON INDUSTRIES LTD.

Inventor： Amos Mizrach ,  Victor Alchanatis ,  Nachshon Shamir ,  Lavi Rosenfeld ,  Clara Shenderey ,  Asher Levi ,  Viacheslav Ostrovsky ,  Menashe Tamir ,  Reuven Shadmi ,  Avishay Tamir

IPC: A01K45/00 ,  G06V40/10

CPC classification number: A01K45/00 ,  G06V40/10 ,  G06V2201/03

Abstract: The present invention provides a system and method for recognizing and segregating chicks by sex according to their feathers pattern. The system and method are based on the instinctive reaction of chicks to spread their wings in order to maintain stability in response to instantaneous disorientation, for example, due to a sudden change in spatial movement. The present invention further features a weighing system and a system for detecting external body defects of chicks for assessing chicks' health.

公开(公告)号：US20230206433A1

公开(公告)日：2023-06-29

申请号：US18056773

申请日：2022-11-18

Applicant: LUNIT INC.

Inventor： Ga Hee PARK ,  Chan Young OCK ,  Kyung Hyun PAENG

IPC: G06T7/00 ,  G06V20/69

CPC classification number: G06T7/0012 ,  G06V20/698 ,  G06V2201/03 ,  G06T2207/30024 ,  G06T2207/30096 ,  G06T2207/30242 ,  G06T2207/10056

Abstract: Provided is a computing apparatus including: at least one memory; and at least one processor, wherein the at least one processor is configured to: perform a first classification on a plurality of tissues expressed in a pathological slide image by analyzing the pathological slide image, perform a second classification on a plurality of cells expressed in a pathological slide image by analyzing the pathological slide image, and calculate tumor purity including information on noise included in the pathological slide image by combining a first classification result and a second classification result.

公开(公告)号：US11682121B2

公开(公告)日：2023-06-20

申请号：US17120983

申请日：2020-12-14

Applicant: Carl Zeiss Meditec, Inc.

Inventor： Homayoun Bagherinia ,  Luis De Sisternes

IPC: G06K9/00 ,  G06T7/181 ,  G06T7/143 ,  G06T7/12 ,  G06V10/44 ,  G06F18/22 ,  G06F18/2415 ,  G06V10/74 ,  G06V10/764

CPC classification number: G06T7/181 ,  G06F18/22 ,  G06F18/2415 ,  G06T7/12 ,  G06T7/143 ,  G06V10/44 ,  G06V10/761 ,  G06V10/764 ,  G06T2200/04 ,  G06T2207/10101 ,  G06T2207/20021 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/30041 ,  G06V2201/03

Abstract: Methods and systems are presented to analyze a retinal image of an eye and assigns features to known anatomical structures such as retinal layers. One example method includes receiving interferometric image data of an eye. A set of features is identified in the image data. A first subset of identified features is associated with known retinal structures using prior knowledge. A first set of characteristic metrics is determined of the first subset of features. A second set of characteristic metrics is determined of a second subset of features. Using the characteristic metrics of the first and the second sets, the second subset of features is associated with the retinal structures. Another example method includes dividing interferometric image data into patches. The image data in each patch is segmented to identify one or more layer boundaries. The segmentation results from each patch are stitched together into a single segmentation dataset.