公开(公告)号：US11666286B2

公开(公告)日：2023-06-06

申请号：US17305105

申请日：2021-06-30

Applicant: BAYLOR RESEARCH INSTITUTE ,  WRIGHT STATE UNIVERSITY

Inventor： Themistocles Dassopoulos ,  Nikolaos Bourbakis

IPC: G06T7/00 ,  G06T11/00 ,  G06V10/141 ,  G06V10/143 ,  G06V10/82 ,  A61B5/00 ,  G16H50/50 ,  G16H50/20 ,  G16H30/40 ,  G06F16/583 ,  G06N20/00 ,  G06N5/04 ,  G06F18/243 ,  G06V10/42 ,  G06V10/764 ,  A61B1/31 ,  G06V10/10

CPC classification number: A61B5/7267 ,  G06F16/583 ,  G06F18/24317 ,  G06N5/04 ,  G06N20/00 ,  G06T7/0014 ,  G06T11/001 ,  G06V10/141 ,  G06V10/143 ,  G06V10/431 ,  G06V10/764 ,  G06V10/82 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50 ,  A61B1/31 ,  G06T2207/30004 ,  G06V10/16 ,  G06V2201/032

Abstract: Computational techniques are applied to video images of polyps to extract features and patterns from different perspectives of a polyp. The extracted features and patterns are synthesized using registration techniques to remove artifacts and noise, thereby generating improved images for the polyp. The generated images of each polyp can be used for training and testing purposes, where a machine learning system separates two types of polyps.

公开(公告)号：US12131466B2

公开(公告)日：2024-10-29

申请号：US17806402

申请日：2022-06-10

Applicant: Tata Consultancy Services Limited

Inventor： Manu Sheoran ,  Meghal Dani ,  Monika Sharma ,  Lovekesh Vig

IPC: G06T7/00 ,  G06V10/25 ,  G06V10/26 ,  G06V10/44 ,  G06V10/778

CPC classification number: G06T7/0012 ,  G06V10/25 ,  G06V10/26 ,  G06V10/454 ,  G06V10/778 ,  G06T2207/10081 ,  G06T2207/30096 ,  G06V2201/032 ,  G06V2201/07

Abstract: State of the art deep network based Universal Lesion Detection (ULD) techniques inherently depend on large number of datasets for training the systems. Moreover, these system are specifically trained for lesion detection in organs of a Region of interest (RoI) of a body. Thus, requires retraining when the RoI varies. Embodiments herein disclose a method and system for domain knowledge augmented multi-head attention based robust universal lesion detection. The method utilizes minimal number of Computer Tomography (CT) scan slices to extract maximum information using organ agnostic HU windows and a convolution augmented attention module for a computationally efficient ULD with enhanced prediction performance.

公开(公告)号：US20240346654A1

公开(公告)日：2024-10-17

申请号：US18754934

申请日：2024-06-26

Applicant: IMIDEX, INC.

Inventor： Richard Vlasimsky

IPC: G06T7/00 ,  A61B6/00 ,  A61B6/50 ,  G06N3/045 ,  G06T3/40 ,  G06T7/11 ,  G06V10/764 ,  G06V10/82 ,  G16H30/40 ,  G16H50/20

CPC classification number: G06T7/0012 ,  A61B6/50 ,  A61B6/5217 ,  G06N3/045 ,  G06T3/40 ,  G06T7/11 ,  G06V10/764 ,  G06V10/82 ,  G16H30/40 ,  G16H50/20 ,  G06T2207/10116 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30061 ,  G06T2207/30096 ,  G06T2207/30168 ,  G06V2201/032

Abstract: The present system provides methods and systems of detecting lung abnormalities in chest x-ray images using at least two neural networks.

公开(公告)号：US20240312183A1

公开(公告)日：2024-09-19

申请号：US18276470

申请日：2022-02-10

Applicant: BeamWorks INC.

Inventor： Jae Il Kim ,  Won Hwa ,  Hye Jung Kim

IPC: G06V10/764 ,  G06V10/25 ,  G06V10/72 ,  G06V10/774

CPC classification number: G06V10/764 ,  G06V10/25 ,  G06V10/72 ,  G06V10/774 ,  G06V2201/032

Abstract: A breast ultrasound diagnosis method using weakly supervised deep-learning artificial intelligence comprises: an ultrasound image preprocessing step of generating input data including only an image region necessary for learning, by deleting personal information about a patient from a breast ultrasound image; a deep-learning step of receiving the input data, obtaining a feature map from the received input data by using a convolutional neural network (CNN) and global average pooling (GAP), and carrying out re-learning; a differential diagnosis step of determining the input data as one of normal, benign, and malignant by using the GAP, and when the input data is determined to be malignant, calculating a probability of malignancy (POM) indicating accuracy of the determination; and a contribution region determination and visualization step of backpropagating a determination result through the CNN, calculating a contribution degree of each pixel that has contributed to the determination result as a gradient and a feature value, and visualizing a contribution region that has contributed to the determination, together with the POM, on the basis of the calculated contribution degree of each pixel, wherein, in the deep-learning step, learning is carried out on the basis of verified performance of the contribution region and the POM.

公开(公告)号：US12089820B2

公开(公告)日：2024-09-17

申请号：US17251768

申请日：2019-06-11

Applicant: COSMO ARTIFICIAL INTELLIGENCE—AI LIMITED

Inventor： Nhan Ngo Dinh ,  Giulio Evangelisti ,  Flavio Navari

IPC: G06K9/00 ,  A61B1/00 ,  A61B1/31 ,  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：US20240257497A1

公开(公告)日：2024-08-01

申请号：US18424021

申请日：2024-01-26

Applicant: Verily Life Sciences LLC

Inventor： Roman Goldenberg ,  Ehud Rivlin ,  Amir Livne ,  Israel Or Weinstein

IPC: G06V10/764 ,  G06V10/82

CPC classification number: G06V10/764 ,  G06V10/82 ,  G06V2201/032

Abstract: Methods, systems, and devices for classifying a target feature in a medical video are presented herein. Some methods may include the steps of: receiving a plurality of frames of the medical video, where the plurality of frames include the target feature; generating, by a first pretrained machine learning model, an embedding vector for each frame of the plurality of frames, each embedding vector having a predetermined number of values; and generating, by a second pretrained machine learning model, a classification of the target feature using the plurality of embedding vectors, where the second pretrained machine learning model analyzes the plurality of embedding vectors jointly.

公开(公告)号：US20240188897A1

公开(公告)日：2024-06-13

申请号：US18556328

申请日：2022-04-21

Applicant: THE UNIVERSITY OF CHICAGO ,  THE TRUSTEES OF THE UNIVERSITY OF PENNSYLVANIA

Inventor： Alexander T. PEARSON ,  James DOLEZAL ,  Devraj BASU ,  Robert BRODY ,  Jalal JALALY

IPC: A61B5/00 ,  G06V10/82

CPC classification number: A61B5/7267 ,  G06V10/82 ,  G06V2201/032

Abstract: An example embodiment involves generating tumor image tiles from images of human papillomavirus positive (HPV +) head and neck squamous cell carcinoma (HNSCC) tumors, wherein the tumor image files are respectively labelled with indicators of tumor recurrence. The example embodiment may further involve training a neural network with the tumor image files as labelled. wherein the training results in the neural network learning combinations of histology features characteristic of tumor recurrence. Further steps may involve providing further tumor image tiles to the trained neural network. the neural network generating classifications of the further tumor image tiles based on likelihood of tumor recurrence. and storing the classifications with as respectively associated with the further tumor image tiles.

公开(公告)号：US11918403B2

公开(公告)日：2024-03-05

申请号：US17548366

申请日：2021-12-10

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

CPC classification number: A61B6/541 ,  A61B6/025 ,  A61B6/032 ,  A61B6/035 ,  A61B6/0407 ,  A61B6/06 ,  A61B6/08 ,  A61B6/4007 ,  A61B6/4014 ,  A61B6/4021 ,  A61B6/405 ,  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: System and method are disclosed for imaging acquisition from sparse partial scans of distributed wide angle. During real time image reconstruction, artificial intelligence (AI) determines if there is enough information to perform diagnostics based on initial scans. If there is enough information from the fractional scans, then data acquisition stops; if more information is needed, then system performs another round of wide-angle sparse scans in a new location progressively until a result is satisfactory. The system reduces X-ray dose on a patient and performs quicker X-ray scan at multiple pulsed source-in-motion tomosynthesis imaging system. The method and system also significantly reduce the amount of time required to display high quality three-dimensional tomosynthesis images.

公开(公告)号：US11823377B2

公开(公告)日：2023-11-21

申请号：US17069708

申请日：2020-10-13

Applicant: CANON KABUSHIKI KAISHA

Inventor： Masami Kawagishi

IPC: G06T7/00 ,  G06T7/12 ,  G16H50/20 ,  G16H30/40 ,  A61B6/00 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  A61B3/12 ,  A61B5/00 ,  A61B5/055 ,  A61B6/03 ,  A61B8/08

CPC classification number: G06T7/0012 ,  A61B6/5217 ,  G06T7/12 ,  G06V10/25 ,  G06V10/764 ,  G06V10/82 ,  G16H30/40 ,  G16H50/20 ,  A61B3/12 ,  A61B5/0095 ,  A61B5/055 ,  A61B6/032 ,  A61B6/037 ,  A61B8/5223 ,  G06T2207/10081 ,  G06T2207/30064 ,  G06V2201/032

Abstract: An information processing apparatus includes an image feature acquiring unit configured to acquire first image features and second image features from a medical image and a deriving unit configured to derive image findings of a plurality of items belonging to a first finding type based on the first image features and deriving image findings of a plurality of items belonging to a second finding type different from the first finding type based on the second image features that at least partly differ from the first image features.

公开(公告)号：US11776243B2

公开(公告)日：2023-10-03

申请号：US17260268

申请日：2019-06-27

Applicant: Nano-X AI Ltd.

Inventor： Amir Bar

IPC: G06V10/82 ,  G06T7/62 ,  G06T7/11 ,  G06F18/24 ,  G06F18/214 ,  G06V10/764

CPC classification number: G06V10/82 ,  G06F18/214 ,  G06F18/24 ,  G06T7/11 ,  G06T7/62 ,  G06V10/764 ,  G06T2207/10081 ,  G06T2207/20084 ,  G06T2207/30008 ,  G06T2207/30028 ,  G06T2207/30052 ,  G06T2207/30096 ,  G06T2207/30101 ,  G06V2201/032

Abstract: There is provided a computer implemented method for identification of an indication of visual object(s) in anatomical image(s) of a target individual, comprising: providing anatomical image(s) of a body portion of a target individual, inputting the anatomical image(s) into a classification component of a neural network (NN) and into a segmentation component of the NN, feeding a size feature into the classification component of the NN, wherein the size feature comprises an indication of a respective size of each segmented visual object identified in the anatomical image(s), the size feature computed according to segmentation data outputted by the segmentation component for each pixel element of the anatomical image(s), and computing, by the classification component of the NN, an indication of visual object(s) in the anatomical image(s).