公开(公告)号：US11810677B2

公开(公告)日：2023-11-07

申请号：US17985114

申请日：2022-11-10

Applicant: Memorial Sloan Kettering Cancer Center

Inventor： Thomas Fuchs ,  Gabriele Campanella

IPC: G16H50/70 ,  G06N20/00 ,  G06T7/00 ,  G16H30/40 ,  G06F18/21 ,  G06F18/2113 ,  G06F18/2415 ,  G06F18/2431 ,  G06V10/762 ,  G06V10/764 ,  G06V10/98

CPC classification number: G16H50/70 ,  G06F18/217 ,  G06F18/2113 ,  G06F18/2415 ,  G06F18/2431 ,  G06N20/00 ,  G06T7/0012 ,  G06V10/764 ,  G06V10/7635 ,  G06V10/98 ,  G16H30/40 ,  G06T2207/10056 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30096 ,  G06V2201/03

Abstract: The present disclosure is directed to systems and methods for classifying biomedical images. A feature classifier may generate a plurality of tiles from a biomedical image. Each tile may correspond to a portion of the biomedical image. The feature classifier may select a subset of tiles from the plurality of tiles by applying an inference model. The subset of tiles may have highest scores. Each score may indicate a likelihood that the corresponding tile includes a feature indicative of the presence of the condition. The feature classifier may determine a classification result for the biomedical image by applying an aggregation model. The classification result may indicate whether the biomedical includes the presence or lack of the condition.

公开(公告)号：US11806090B2

公开(公告)日：2023-11-07

申请号：US17679241

申请日：2022-02-24

Applicant: MAKO Surgical Corp.

Inventor： Jean Stawiaski ,  Fadi Ghanam ,  David Hofmann

IPC: G06T7/73 ,  A61B34/20 ,  A61B34/30 ,  A61B17/16 ,  A61B34/00 ,  A61B34/10 ,  G06T7/00

CPC classification number: A61B34/20 ,  A61B17/16 ,  A61B34/10 ,  A61B34/30 ,  A61B34/70 ,  G06T7/00 ,  G06T7/74 ,  A61B2017/1602 ,  A61B2034/101 ,  A61B2034/107 ,  A61B2034/2051 ,  A61B2034/2063 ,  A61B2034/2065 ,  A61B2034/2068 ,  G06V2201/03

Abstract: Systems and methods for calibration of a surgical tool having a tracker. At least one camera captures images of the tracker at a first exposure and captures images of the surgical tool at a second exposure with a different exposure time and/or illumination than the first exposure. Controller(s) recognize a pose of the tracker based on the first exposure images and recognize a geometry of the surgical tool based on the second exposure images. The controller(s) correlate the recognized pose of the tracker and the recognized geometry of the surgical tool to define a relationship between the tracker and the surgical tool and calibrate the surgical tool based on the defined relationship.

公开(公告)号：US20230351599A1

公开(公告)日：2023-11-02

申请号：US18329024

申请日：2023-06-05

Applicant: PAIGE.AI, Inc.

Inventor： Danielle GORTON ,  Patricia RACITI ,  Jillian SUE ,  Razik YOUSFI

IPC: G16H15/00 ,  G06T11/60 ,  G06T7/00 ,  G06V10/25 ,  G06V10/77 ,  G16H30/40 ,  G16H80/00 ,  G06V10/12 ,  G16H10/40 ,  G16H50/20

CPC classification number: G06T7/0014 ,  G06T11/60 ,  G06V10/12 ,  G06V10/25 ,  G06V10/7715 ,  G16H10/40 ,  G16H15/00 ,  G16H30/40 ,  G16H50/20 ,  G16H80/00 ,  G06T2207/10004 ,  G06T2207/30004 ,  G06T2207/30024 ,  G06V2201/03

Abstract: A computer-implemented method of using a machine learning model to categorize a sample in digital pathology may include receiving one or more cases, each associated with digital images of a pathology specimen; identifying, using the machine learning model, a case as ready to view; receiving a selection of the case, the case comprising a plurality of parts; determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious; receiving a selection of a part of the plurality of parts; determining whether a plurality of slides associated with the part are suspicious or non-suspicious; determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides.

公开(公告)号：US20230342924A1

公开(公告)日：2023-10-26

申请号：US18138688

申请日：2023-04-24

Applicant: 2692873 Ontario Inc.

Inventor： Trevor CHAMPAGNE

IPC: G06T7/00 ,  G06V10/764 ,  G06V10/82 ,  G06T7/155 ,  G06V10/774 ,  G06V20/70

CPC classification number: G06T7/0012 ,  G06V10/764 ,  G06V10/82 ,  G06T7/155 ,  G06V10/774 ,  G06V20/70 ,  G06V2201/03 ,  G06T2207/30088 ,  G06T2207/20021 ,  G06T2207/20036 ,  G06T2207/20084 ,  G06V20/41

Abstract: Systems and methods using machine learning for classifying samples, for example medical images such as dermatological images. The method can use contrapositive logic principals. An example of the method includes: receiving a sample; generating, by a first neural network using the sample: a first classification of a positive label versus a negative label; generating, by a second neural network using the sample: a second classification of the negative label versus not the negative label; and generating, by a category classification module using the first classification and the second classification: a category of the sample.

公开(公告)号：US20230342913A1

公开(公告)日：2023-10-26

申请号：US17660717

申请日：2022-04-26

Applicant: GE Precision Healthcare LLC

Inventor： Mahendra Madhukar Patil ,  Rakesh Mullick ,  Sudhanya Chatterjee ,  Syed Asad Hashmi ,  Dattesh Dayanand Shanbhag ,  Deepa Anand ,  Suresh Emmanuel Devadoss Joel

IPC: G06T7/00 ,  G06N20/00 ,  G06V10/25

CPC classification number: G06T7/0012 ,  G06N20/00 ,  G06V10/25 ,  G06V2201/03

Abstract: Techniques are described for generating high quality training data collections for training artificial intelligence (AI) models in the medical imaging domain. A method embodiment comprises receiving, by a system comprising processor, input indicating a clinical context associated with usage of a medical image dataset, and selecting, by the system, one or more data scrutiny metrics for filtering the medical image dataset based on the clinical context. The method further comprises applying, by the system, one or more image processing functions to the medical image dataset to generate metric values of the one or more data scrutiny metrics for respective medical images included in the medical image dataset, filtering, by the system, the medical image dataset into one or more subsets based on one or more acceptability criteria for the metric values.

公开(公告)号：US11798686B2

公开(公告)日：2023-10-24

申请号：US17282775

申请日：2019-10-04

Applicant: DEEP BIO INC.

Inventor： Tae Yeong Kwak ,  Sang Hun Lee ,  Sun Woo Kim

IPC: G06K9/00 ,  G16H50/20 ,  G16H30/40 ,  G06N3/088 ,  G06T7/00 ,  G06N3/045 ,  G06F18/214

CPC classification number: G16H50/20 ,  G06N3/045 ,  G06N3/088 ,  G06T7/0014 ,  G16H30/40 ,  G06F18/2148 ,  G06T2207/30081 ,  G06V2201/03

Abstract: A system for searching for a pathological image includes: an autoencoder having an encoder for receiving an original pathological image and extracting a feature of the original pathological image, and a decoder for receiving the feature of the original pathological image extracted by the encoder and generating a reconstructed pathological image corresponding to the original pathological image; a diagnostic neural network for receiving the reconstructed pathological image generated by the autoencoder that has received the original pathological image, and outputting a diagnosis result of a predetermined disease; and a training module for training the autoencoder and the diagnostic neural network by inputting a plurality of training pathological images, each labeled with a diagnosis result, into the autoencoder. The autoencoder is trained by reflecting the diagnosis result of the reconstructed pathological image output from the diagnostic neural network.

公开(公告)号：US11798650B2

公开(公告)日：2023-10-24

申请号：US16160968

申请日：2018-10-15

Applicant: Illumina, Inc.

Inventor： Laksshman Sundaram ,  Kai-How Farh ,  Hong Gao ,  Jeremy Francis McRae

IPC: G06N3/08 ,  G06N7/00 ,  G16B40/00 ,  G16B20/00 ,  G16H70/60 ,  G06N3/04 ,  G06K9/62 ,  G06N3/084 ,  G06F18/24 ,  G06F18/214 ,  G06N3/045 ,  G06N3/048 ,  G06N7/01

CPC classification number: G16B20/00 ,  G06F18/2148 ,  G06F18/2155 ,  G06F18/24 ,  G06N3/045 ,  G06N3/048 ,  G06N3/08 ,  G06N3/084 ,  G06N7/01 ,  G16B40/00 ,  G16H70/60 ,  G06V2201/03

Abstract: The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional neural network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.

公开(公告)号：US11798168B2

公开(公告)日：2023-10-24

申请号：US17821481

申请日：2022-08-23

Applicant: SHANGHAI UNITED IMAGING HEALTHCARE CO., LTD.

Inventor： Yu Xiang ,  Yang Zhang

IPC: G06K9/00 ,  G06T7/11 ,  G06T7/00 ,  G06T7/187 ,  G06T7/136 ,  G06T7/155 ,  G06V10/25 ,  G06F18/25 ,  G06V10/80 ,  G06V10/20

CPC classification number: G06T7/11 ,  G06F18/25 ,  G06T7/0012 ,  G06T7/136 ,  G06T7/155 ,  G06T7/187 ,  G06V10/25 ,  G06V10/255 ,  G06V10/803 ,  G06T2207/10016 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/10108 ,  G06T2207/10116 ,  G06T2207/20076 ,  G06T2207/20104 ,  G06T2207/20221 ,  G06T2207/30004 ,  G06V2201/03

Abstract: A method and system for image processing is provided. The method for image processing may include: obtaining an image data set, wherein the image data set includes a first set of volume data; and determining, by the at least one processor, a target anatomy of interest based on the first set of volume data. The determining of the target anatomy of interest may include: determining an initial anatomy of interest in the first set of volume data; and editing the initial anatomy of interest to obtain the target anatomy of interest. The target anatomy of interest may include at least one region of interest (ROI) or at least one volume of interest (VOI). The initial anatomy of interest may include at least one ROI or at least one VOI.

公开(公告)号：US20230326582A1

公开(公告)日：2023-10-12

申请号：US18323553

申请日：2023-05-25

Applicant: Case Western Reserve University

Inventor： Pranjal Vaidya ,  Anant Madabhushi ,  Kaustav Bera

IPC: G16H50/20 ,  G06V10/764 ,  G06V10/44 ,  G06F18/214 ,  G06V10/80 ,  G16H30/40 ,  G06T7/00 ,  G06V10/774 ,  G06V10/82

CPC classification number: G16H30/40 ,  G06F18/214 ,  G06T7/0012 ,  G06V10/454 ,  G06V10/764 ,  G06V10/7747 ,  G06V10/811 ,  G06V10/82 ,  G16H50/20 ,  G06T2207/10072 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30061 ,  G06T2207/30096 ,  G06V2201/03

Abstract: The present disclosure, in some embodiments, relates to a method. The method includes using a first machine learning model to generate a first medical prediction associated with a lesion in a medical scan using one or more intra-lesional radiomic features associated with the lesion and the one or more peri-lesional radiomic features associated with a peri-lesional region around the lesion. A second machine learning model is used to generate a second medical prediction associated with the lesion using one or more pathomic features associated with the lesion. A combined medical prediction associated with the lesion is generated using the first medical prediction and the second medical prediction as inputs to a third model.

公开(公告)号：US20230326172A1

公开(公告)日：2023-10-12

申请号：US18133293

申请日：2023-04-11

Applicant: VAIM Technologies LLC

Inventor： Manjeet Dhariwal ,  Aaron Feiler

IPC: G06V10/42 ,  G06V10/70 ,  G10L15/02 ,  G10L15/06 ,  G10L15/26 ,  G10L15/22 ,  G10L25/57 ,  G16H15/00

CPC classification number: G06V10/42 ,  G06V10/70 ,  G10L15/02 ,  G10L15/063 ,  G10L15/26 ,  G10L15/22 ,  G10L25/57 ,  G16H15/00 ,  G06V2201/03

Abstract: Systems and methods for automatically extracting one or more salient images from a surgical video stream are described. A plurality of records including annotated images from recorded surgical procedures are used as training data to generate an image extraction machine learning model. Features, extracted from the training data, are used as inputs to the image extraction machine learning model in a training phase, which outputs salient images. After training, features extracted from the surgical video stream are input into the trained image extraction machine learning model to output the one or more salient images from the surgical video stream.