公开(公告)号：US11813095B2

公开(公告)日：2023-11-14

申请号：US17127302

申请日：2020-12-18

Applicant: CANON KABUSHIKI KAISHA

Inventor： Sota Torii ,  Atsushi Iwashita ,  Takeshi Noda ,  Kosuke Terui ,  Akira Tsukuda

IPC: A61B6/00 ,  G01T1/161 ,  G01T1/17 ,  G06F18/22 ,  G06V10/98 ,  G06T7/00 ,  G06T11/00

CPC classification number: A61B6/4241 ,  A61B6/461 ,  A61B6/482 ,  A61B6/54 ,  G01T1/161 ,  G01T1/17 ,  G06F18/22 ,  G06T7/0012 ,  G06T11/00 ,  G06V10/993 ,  A61B6/5258 ,  G06T2207/10116 ,  G06T2207/30004 ,  G06V2201/03

Abstract: A radiation imaging apparatus comprises a generating unit configured to generate a material characteristic image with respect to a plurality of materials included in a radiation image that has been captured using different radiation energies; and a reconstructing unit configured to set different radiation energies for the respective plurality of materials, and to generate a reconstructed image based on monochromatic radiation images of the respective materials, the monochromatic radiation images being based on the different radiation energies.

公开(公告)号：US20230360385A1

公开(公告)日：2023-11-09

申请号：US18170621

申请日：2023-02-17

Applicant: City University of Hong Kong

Inventor： Ho Sang Lam ,  Wai Ming Peter Tsang

IPC: G06V10/82 ,  G03H1/16 ,  G06V10/46

CPC classification number: G06V10/82 ,  G03H1/16 ,  G06V10/478 ,  G06V2201/03

Abstract: A computer-implemented method and a system for object identification and/or classification. The computer-implemented method includes receiving digital hologram data of a digital hologram of an object. The digital hologram data comprises phase information and magnitude information. The computer-implemented method further includes processing the digital hologram data based on a neural-network-based ensemble model to identify and/or classify the object.

公开(公告)号：US11810298B2

公开(公告)日：2023-11-07

申请号：US17971312

申请日：2022-10-21

Applicant: Salesforce, Inc.

Inventor： Nikhil Naik ,  Ali Madani ,  Nitish Shirish Keskar

IPC: G06T7/00 ,  G16H50/20 ,  G06N5/04 ,  G16H10/20 ,  G06N20/00 ,  G06F18/21 ,  G06F18/214 ,  G06V20/69

CPC classification number: G06T7/0012 ,  G06F18/217 ,  G06F18/2148 ,  G06N5/04 ,  G06N20/00 ,  G06V20/69 ,  G16H10/20 ,  G16H50/20 ,  G06V2201/03

Abstract: An analytics system uses one or more machine-learned models to predict a hormone receptor status from a H&E stain image. The system partitions H&E stain images each into a plurality of non-overlapping image tiles. Bags of tiles are created through sampling of the image tiles. For each H&E stain image, the system generates a feature vector from a bag of tiles sampled from the partitioned image tiles. The analytics system trains one or more machine-learned models with training H&E stain images having a positive or negative receptor status. With the trained models, the analytics system predicts a hormone receptor status by applying a prediction model to the feature vector for a test H&E stain image.

公开(公告)号：US11810292B2

公开(公告)日：2023-11-07

申请号：US17038934

申请日：2020-09-30

Applicant: Case Western Reserve University

Inventor： Anant Madabhushi ,  Nathaniel Braman ,  Jeffrey Eben

IPC: G06T7/00 ,  G06T7/11 ,  G06V10/764 ,  G06V10/771 ,  G06V10/774 ,  G06V10/80 ,  G06V10/82 ,  G06N20/10 ,  G06F17/16 ,  G06V10/20 ,  G06V10/776

CPC classification number: G06T7/0012 ,  G06F17/16 ,  G06N20/10 ,  G06T7/11 ,  G06V10/255 ,  G06V10/764 ,  G06V10/771 ,  G06V10/774 ,  G06V10/776 ,  G06V10/809 ,  G06V10/82 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30096 ,  G06V2201/03

Abstract: Embodiments discussed herein facilitate training and/or employing a combined model employing machine learning and deep learning outputs to generate prognoses for treatment of tumors. One example embodiment can extract radiomic features from a tumor and a peri-tumoral region; provide the intra-tumoral and peri-tumoral features to two separate machine learning models; provide the segmented tumor and peri-tumoral region to two separate deep learning models; receive predicted prognoses from each of the machine learning models and each of the deep learning models; provide the predicted prognoses to a combined machine learning model; and receive a combined predicted prognosis for the tumor from the combined machine learning model.

公开(公告)号：US20230343079A1

公开(公告)日：2023-10-26

申请号：US17868858

申请日：2022-07-20

Applicant: Verb Surgical Inc.

Inventor： Nishant Shailesh Sahni ,  Peijmon Kasravi ,  Darrick Tyler Sturgeon ,  Jocelyn Elaine Barker

IPC: G06V10/778 ,  G06V20/70 ,  G06V10/94 ,  G06F3/0482 ,  G06F3/0488 ,  G06V10/774

CPC classification number: G06V10/7788 ,  G06V20/70 ,  G06V10/945 ,  G06F3/0482 ,  G06F3/0488 ,  G06V10/774 ,  G06V2201/03

Abstract: An annotation system facilitates collection of labels for images, video, or other content items relevant to training machine learning models associated with surgical applications or other medical applications. The annotation system enables an administrator to configure annotation jobs associated with training a machine learning model. The job configuration controls presentation of content items to various participating annotators via an annotation application and collection of the labels via a user interface of the annotation application. The annotation application enables the participating annotators to provide inputs in a simple and efficient manner, such as by providing gesture-based inputs or selecting graphical elements associated with different possible labels.

公开(公告)号：US20230343063A1

公开(公告)日：2023-10-26

申请号：US18216918

申请日：2023-06-30

Applicant: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

Inventor： Zhe XU ,  Donghuan LU ,  Kai MA ,  Yefeng ZHENG

IPC: G06V10/26 ,  G06V20/70 ,  G06V10/82 ,  G06V10/77 ,  G06V10/74 ,  G06T7/194 ,  G06V10/776 ,  G06V10/774

CPC classification number: G06V10/267 ,  G06V20/70 ,  G06V10/82 ,  G06V10/7715 ,  G06V10/761 ,  G06T7/194 ,  G06V10/776 ,  G06V10/774 ,  G06T2207/20084 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06V2201/03 ,  G06T2207/30096 ,  G06T2207/30016 ,  G06T2207/30084

Abstract: An image segmentation model training method includes acquiring a first image, a second image, and a labeled image of the first image; acquiring a first predicted image according to a first network model; acquiring a second predicted image according to a second network model; determining a reference image of the second image based on the second image and the labeled image of the first image; and updating a model parameter of the first network model based on the first predicted image, the labeled image, the second predicted image, and the reference image to obtain an image segmentation model.

公开(公告)号：US11790637B2

公开(公告)日：2023-10-17

申请号：US17500213

申请日：2021-10-13

Applicant: Gauss Surgical, Inc.

Inventor： Siddarth Satish ,  Kevin J. Miller ,  Andrew T. Hosford

IPC: G06V10/764 ,  G06T7/62 ,  A61B5/00 ,  A61B5/103 ,  A61B5/145 ,  A61B5/1455 ,  G06V10/56 ,  G06F18/24 ,  A61B5/02 ,  G06T7/00

CPC classification number: G06V10/764 ,  A61B5/0035 ,  A61B5/0082 ,  A61B5/02042 ,  A61B5/1032 ,  A61B5/14546 ,  A61B5/14551 ,  G06F18/24 ,  G06T7/0012 ,  G06T7/62 ,  G06V10/56 ,  A61B2505/05 ,  A61B2576/02 ,  G06T2207/10024 ,  G06T2207/10028 ,  G06T2207/10048 ,  G06T2207/30004 ,  G06T2207/30104 ,  G06T2207/30242 ,  G06V2201/03

Abstract: Systems and methods for detecting, counting and analyzing the blood content of a surgical textile are provided, utilizing an infrared or depth camera in conjunction with a color image.

公开(公告)号：US11790532B2

公开(公告)日：2023-10-17

申请号：US17259519

申请日：2019-07-10

Applicant: DENTAL MONITORING

Inventor： Philippe Salah ,  Thomas Pellissard ,  Guillaume Ghyselinck ,  Laurent Debraux

IPC: G06T7/00 ,  G06T7/11 ,  G16H50/50 ,  G06F30/27 ,  A61C7/00 ,  G06N3/08

CPC classification number: G06T7/11 ,  G06F30/27 ,  G06T7/0012 ,  G16H50/50 ,  A61C7/002 ,  G06N3/08 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30036 ,  G06V2201/03

Abstract: Method for cutting a three-dimensional model of a dental scene, or “scene model.” The method includes acquiring a view of the scene model, called the “analysis view.” The method includes analyzing the analysis view by a neural network in order to identify, in the analysis view, at least one elementary zone representing an element of the dental scene, and assigning a value to at least one attribute of the elementary zone. The method includes identifying a region of the scene model represented by the elementary zone on the analysis view, and assigning, in the region, a value to an attribute of the scene model in accordance with the value of the attribute of the elementary zone.

公开(公告)号：US11783603B2

公开(公告)日：2023-10-10

申请号：US16958555

申请日：2018-03-07

Applicant: VERILY LIFE SCIENCES LLC.

Inventor： Martin Stumpe ,  Philip Nelson ,  Lily Peng

IPC: G06K9/62 ,  G06V20/69 ,  G16H30/40 ,  G01N1/30 ,  G06N3/08 ,  G06T7/00 ,  G06T11/00 ,  G06F18/214 ,  G06V10/82

CPC classification number: G06V20/69 ,  G01N1/30 ,  G06F18/214 ,  G06N3/08 ,  G06T7/0012 ,  G06T11/001 ,  G06V10/82 ,  G06V20/695 ,  G16H30/40 ,  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：US11783476B2

公开(公告)日：2023-10-10

申请号：US17079957

申请日：2020-10-26

Applicant: DeepHealth, Inc.

Inventor： William Lotter

IPC: G06T7/00 ,  G06K9/00 ,  G06K9/32 ,  G06T5/40 ,  G16H50/20 ,  A61B6/00 ,  A61B6/02 ,  G06N3/04 ,  G06V20/64 ,  G06N3/045

CPC classification number: G06T7/0012 ,  A61B6/025 ,  A61B6/502 ,  G06N3/045 ,  G06T5/40 ,  G06V20/647 ,  G16H50/20 ,  G06T2207/10112 ,  G06T2207/20084 ,  G06T2207/30068 ,  G06V2201/03

Abstract: The present disclosure provides a method for determining a malignancy likelihood score for breast tissue of a patient. The method includes receiving a plurality of two-dimensional images of the breast tissue, the two-dimensional images being derived from a three-dimensional image of the breast tissue, for each two-dimensional image, providing the two-dimensional image to a first model including a first trained neural network, and receiving a number of indicators from the first model, each indicator being associated with a two-dimensional image included in the plurality of two-dimensional images, generating a synthetic two-dimensional image based on the number of indicators and at least one of the plurality of two-dimensional images, providing the synthetic two-dimensional image to a second model including a second trained neural network, receiving a malignancy likelihood score from the second model, and outputting a report including the malignancy likelihood score to at least one of a memory or a display.