公开(公告)号：US20240242813A1

公开(公告)日：2024-07-18

申请号：US18568000

申请日：2021-06-17

Applicant: Elekta Limited

Inventor： Francois Hebert ,  Sebastien Tremblay

IPC: G16H20/40 ,  G06T7/00 ,  G06V10/74 ,  G16H30/40

CPC classification number: G16H20/40 ,  G06T7/0012 ,  G06V10/761 ,  G16H30/40 ,  G06T2207/10072 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30096 ,  G06V2201/032

Abstract: Systems and methods are disclosed for performing operations comprising: receiving a plurality of training medical images used to train a machine learning technique; applying a model to the plurality of training medical images to generate a distribution representation of the plurality of training medical images; computing a first distance between a given training medical image in the plurality of training medical images and the distribution representation; computing a second distance between a new medical image and the distribution representation; and computing a quality factor indicating a measure of similarity between a new medical image and the plurality of medical images as a function of the first distance and the second distance.

公开(公告)号：US20240221373A1

公开(公告)日：2024-07-04

申请号：US18288380

申请日：2022-04-20

Applicant: DEEP BIO INC.

Inventor： Jun Young CHOI ,  Tae Yeong KWAK ,  Sun Woo KIM

IPC: G06V10/82 ,  G06T7/00 ,  G16H50/20

CPC classification number: G06V10/82 ,  G06T7/0012 ,  G16H50/20 ,  G06T2207/30068 ,  G06T2207/30096 ,  G06V2201/032

Abstract: A training method for training an artificial neural network capable of determining a breast cancer lesion area in consideration of both microscopic features and macroscopic features of biological tissue, and a computing system for performing same. A method is provided for training an artificial neural network, comprising steps in which: an artificial neural network training system acquires a slide image of a biological tissue slide; the artificial neural network training system acquires, from the slide image, a first high-resolution patch to an Nth high-resolution patch; the artificial neural network training system acquires an ith low-resolution patch corresponding to an ith high-resolution patch (1

公开(公告)号：US12026234B2

公开(公告)日：2024-07-02

申请号：US17251773

申请日：2019-06-11

Applicant: COSMO ARTIFICIAL INTELLIGENCE—AI LIMITED

Inventor： Nhan Ngo Dinh ,  Giulio Evangelisti ,  Flavio Navari

IPC: G06F18/2413 ,  A61B1/00 ,  A61B1/273 ,  A61B1/31 ,  G06F18/21 ,  G06F18/214 ,  G06F18/40 ,  G06N3/045 ,  G06N3/08 ,  G06N3/088 ,  G06T7/00 ,  G16H30/40 ,  G16H50/20

CPC classification number: G06F18/2413 ,  A61B1/000096 ,  A61B1/273 ,  A61B1/2736 ,  A61B1/31 ,  G06F18/214 ,  G06F18/2148 ,  G06F18/217 ,  G06F18/41 ,  G06N3/045 ,  G06N3/08 ,  G06N3/088 ,  G06T7/0012 ,  G16H30/40 ,  G16H50/20 ,  G06T2207/10016 ,  G06T2207/10068 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30032 ,  G06T2207/30096 ,  G06V2201/032

Abstract: The present disclosure relates to computer-implemented systems and methods for training and using generative adversarial networks. In one implementation, a system for training a generative adversarial network may include at least one processor that may provide a first plurality of images including representations of a feature-of-interest and indicators of locations of the feature-of-interest and use the first plurality and indicators to train an object detection network. Further, the processor(s) may provide a second plurality of images including representations of the feature-of-interest, and apply the trained object detection network to the second plurality to produce a plurality of detections of the feature-of-interest. Additionally, the processor(s) may provide manually set verifications of true positives and false positives with respect to the plurality of detections, use the verifications to train a generative adversarial network, and retrain the generative adversarial network using at least one further set of images, further detections, and further manually set verifications.

公开(公告)号：US20240202917A1

公开(公告)日：2024-06-20

申请号：US18529511

申请日：2023-12-05

Applicant: Johnson & Johnson Enterprise Innovation Inc.

Inventor： George R. Washko, JR. ,  Christopher Scott Stevenson ,  Samuel Yoffe Ash ,  Raul San Jose Estepar ,  Matthew David Mailman

IPC: G06T7/00 ,  A61B6/00 ,  A61B6/03 ,  A61B6/50 ,  G06V10/40 ,  G16H30/40 ,  G16H50/30 ,  G16H50/50

CPC classification number: G06T7/0012 ,  A61B6/032 ,  A61B6/50 ,  A61B6/5217 ,  G06T7/0014 ,  G06V10/40 ,  G16H30/40 ,  G16H50/30 ,  G16H50/50 ,  G06T2207/10081 ,  G06T2207/10116 ,  G06T2207/20076 ,  G06T2207/30061 ,  G06T2207/30096 ,  G06V2201/032

Abstract: Risk prediction models are trained and deployed to analyze images, such as computed tomography scans, for predicting future risk of lung cancer for one or more subjects. Individual risk prediction models are separately trained on nodule-specific and non-nodule specific features such that each risk prediction model can predict future risk of lung cancer across different time periods (e.g., 1 year, 3 years, or 5 years). Such risk prediction models are useful for developing preventive therapies for lung cancer by enabling clinical trial enrichment.

公开(公告)号：US12008758B2

公开(公告)日：2024-06-11

申请号：US17952234

申请日：2022-09-24

Applicant: PSIP LLC

Inventor： Salmaan Hameed ,  Giau Nguyen

IPC: G06T7/00 ,  A61B1/00 ,  G01B11/03 ,  G01B11/30 ,  G06F18/2413 ,  G06V10/44 ,  G06V10/50 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06T7/0014 ,  A61B1/000094 ,  A61B1/000096 ,  A61B1/0014 ,  A61B1/00177 ,  A61B1/00181 ,  G01B11/03 ,  G01B11/30 ,  G06V10/454 ,  G06V10/50 ,  G06V10/764 ,  G06V10/82 ,  A61B1/00101 ,  G06F18/2414 ,  G06T2207/10068 ,  G06T2207/30032 ,  G06V2201/032 ,  G06V2201/034

Abstract: Identifying polyps or lesions in a colon. In some variations, computer-implemented methods for polyp detection may be used in conjunction with an endoscope system to analyze the images captured by the endoscopic system, identify any polyps and/or lesions in a visual scene captured by the endoscopic system, and provide an indication to the practitioner that a polyp and/or lesion has been detected.

公开(公告)号：US20240108209A1

公开(公告)日：2024-04-04

申请号：US18525800

申请日：2023-11-30

Applicant: COSMO ARTIFICIAL INTELLIGENCE - AI LIMITED

Inventor： NHAN NGO DINH ,  GIULIO EVANGELISTI ,  FLAVIO NAVARI

IPC: A61B1/31 ,  A61B1/00 ,  A61B5/00 ,  G06F18/214 ,  G06N3/045 ,  G06N3/08 ,  G06T7/00 ,  G06T7/70 ,  G06T11/00 ,  G06T11/20 ,  G06T11/60 ,  G06V10/20 ,  G06V10/25 ,  G06V10/82 ,  G06V20/40 ,  G16H30/20

CPC classification number: A61B1/31 ,  A61B1/000094 ,  A61B1/000095 ,  A61B1/000096 ,  A61B1/00055 ,  A61B5/7264 ,  A61B5/7267 ,  G06F18/214 ,  G06N3/045 ,  G06N3/08 ,  G06T7/0012 ,  G06T7/70 ,  G06T11/001 ,  G06T11/203 ,  G06T11/60 ,  G06V10/25 ,  G06V10/255 ,  G06V10/82 ,  G06V20/40 ,  G06V20/49 ,  G16H30/20 ,  G06T2207/10016 ,  G06T2207/10068 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06T2207/30032 ,  G06T2207/30064 ,  G06T2207/30096 ,  G06V2201/03 ,  G06V2201/032

Abstract: The present disclosure relates to systems and methods for processing real-time video and detecting objects in the video. In one implementation, a system is provided that includes an input port for receiving real-time video obtained from a medical image device, a first bus for transferring the received real-time video, and at least one processor configured to receive the real-time video from the first bus, perform object detection by applying a trained neural network on frames of the received real-time video, and overlay a border indicating a location of at least one detected object in the frames. The system also includes a second bus for receiving the video with the overlaid border, an output port for outputting the video with the overlaid border from the second bus to an external display, and a third bus for directly transmitting the received real-time video to the output port.

公开(公告)号：US20240008712A1

公开(公告)日：2024-01-11

申请号：US18036718

申请日：2021-11-11

Applicant: HOYA CORPORATION

Inventor： Hannes SEIBT

IPC: A61B1/00 ,  A61B1/04 ,  G06V10/74

CPC classification number: A61B1/000094 ,  A61B1/000096 ,  A61B1/04 ,  G06V10/761 ,  G06V2201/07 ,  G06V2201/032

Abstract: An information content of a section of a current image of a series of images of a video signal is calculated, wherein the video signal has to be fed to an algorithm for calculating and indicating detections of objects in the video signal. If the calculated information content of the section of the current image is smaller than a threshold value, the calculation and indication of detections of objects for the section of at least the current image or the current image and further images of the series of images is suppressed.

公开(公告)号：US11844499B2

公开(公告)日：2023-12-19

申请号：US18165232

申请日：2023-02-06

Applicant: COSMO ARTIFICIAL INTELLIGENCE—AI LIMITED

Inventor： Nhan Ngo Dinh ,  Giulio Evangelisti ,  Flavio Navari

IPC: G06K9/62 ,  A61B1/31 ,  G06T7/70 ,  G16H30/20 ,  G06N3/08 ,  G06T7/00 ,  G06T11/00 ,  G06T11/60 ,  G06T11/20 ,  A61B1/00 ,  A61B5/00 ,  G06V20/40 ,  G06V10/82 ,  G06F18/214 ,  G06N3/045 ,  G06V10/25 ,  G06V10/20

CPC classification number: A61B1/31 ,  A61B1/00055 ,  A61B1/000094 ,  A61B1/000095 ,  A61B1/000096 ,  A61B5/7264 ,  A61B5/7267 ,  G06F18/214 ,  G06N3/045 ,  G06N3/08 ,  G06T7/0012 ,  G06T7/70 ,  G06T11/001 ,  G06T11/203 ,  G06T11/60 ,  G06V10/25 ,  G06V10/255 ,  G06V10/82 ,  G06V20/40 ,  G06V20/49 ,  G16H30/20 ,  G06T2207/10016 ,  G06T2207/10068 ,  G06T2207/20084 ,  G06T2207/30004 ,  G06T2207/30032 ,  G06T2207/30064 ,  G06T2207/30096 ,  G06V2201/03 ,  G06V2201/032

Abstract: The present disclosure relates to systems and methods for processing real-time video and detecting objects in the video. In one implementation, a system is provided that includes an input port for receiving real-time video obtained from a medical image device, a first bus for transferring the received real-time video, and at least one processor configured to receive the real-time video from the first bus, perform object detection by applying a trained neural network on frames of the received real-time video, and overlay a border indicating a location of at least one detected object in the frames. The system also includes a second bus for receiving the video with the overlaid border, an output port for outputting the video with the overlaid border from the second bus to an external display, and a third bus for directly transmitting the received real-time video to the output port.

公开(公告)号：US20230334665A1

公开(公告)日：2023-10-19

申请号：US18338352

申请日：2023-06-21

Applicant: FUJIFILM Corporation

Inventor： Yuta Hiasa

IPC: G06T7/00 ,  G06V10/82

CPC classification number: G06T7/0012 ,  G06V10/82 ,  G06V2201/032 ,  G06V2201/031 ,  G06T2207/30064 ,  G06T2207/20081

Abstract: A first processor of a learning device reads out a first medical use image having a disease label from a data set stored in a memory and inputs the read out first medical use image to a first learning model. The first medical use image is normalized based on a lung field region extracted by the first learning model, and a second learning model that has not been trained and detects a disease is trained by using the normalized first medical use image and the disease label. In a case in which the second learning model is trained, a value of the uncertainty of the first medical use image is calculated based on the uncertainty simultaneously estimated by the first learning model, and the first medical use image having a large value of uncertainty is excluded from learning data.

公开(公告)号：US11766231B2

公开(公告)日：2023-09-26

申请号：US17523373

申请日：2021-11-10

Applicant: Jianqiang Liu ,  Manat Maolinbay ,  Chwen-yuan Ku ,  Linbo Yang

Inventor： Jianqiang Liu ,  Manat Maolinbay ,  Chwen-yuan Ku ,  Linbo Yang

IPC: A61B6/00 ,  A61B6/02 ,  G06T17/00 ,  G01N23/044 ,  A61B6/03 ,  A61B6/06 ,  G01N23/083 ,  G01N23/18 ,  G06T7/00 ,  G06T11/00 ,  A61B6/04 ,  G06T7/11 ,  G16H10/60 ,  G16H30/20 ,  G16H50/20 ,  G06V10/25 ,  G06V10/62 ,  A61B6/08

CPC classification number: A61B6/541 ,  A61B6/025 ,  A61B6/032 ,  A61B6/035 ,  A61B6/0407 ,  A61B6/06 ,  A61B6/08 ,  A61B6/405 ,  A61B6/4007 ,  A61B6/4014 ,  A61B6/4021 ,  A61B6/4208 ,  A61B6/4283 ,  A61B6/4405 ,  A61B6/4441 ,  A61B6/4452 ,  A61B6/4476 ,  A61B6/4482 ,  A61B6/467 ,  A61B6/482 ,  A61B6/54 ,  A61B6/542 ,  A61B6/56 ,  A61B6/583 ,  G01N23/044 ,  G01N23/083 ,  G01N23/18 ,  G06T7/0012 ,  G06T7/0016 ,  G06T7/11 ,  G06T11/003 ,  G06T11/006 ,  G06T17/00 ,  G06V10/25 ,  G06V10/62 ,  G16H10/60 ,  G16H30/20 ,  G16H50/20 ,  A61B6/4275 ,  A61B6/502 ,  G01N2223/401 ,  G06T2200/24 ,  G06T2207/10076 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30064 ,  G06T2207/30096 ,  G06T2207/30168 ,  G06T2210/41 ,  G06V2201/032

Abstract: A system and method for improved image acquisition of multiple pulsed X-ray source-in-motion tomosynthesis imaging apparatus by generating the electrocardiogram (ECG) waveform data using an ECG device. Once a representative cardiac cycle is determined, system will acquire images only at rest period of heart beat. Real time ECG waveform is used as ECG synchronization for image improvement. The imaging apparatus avoids ECG peak pulse for better chest, lung and breast imaging under influence of cardiac periodical motion. As a result, smoother data acquisition, much higher data quality can be achieved. The multiple pulsed X-ray source-in-motion tomosynthesis machine is with distributed multiple X-ray sources that is spanned at wide scan angle. At rest period of one heartbeat, multiple X-ray exposures are acquired from X-ray sources at different angles. The machine itself has capability to acquire as many as 60 actual projection images within about two seconds.