Ge Precision Healthcare LLC

General Electric Company

Omnyx, LLC

James Gualtieri

Rakesh Mullick

Ravi Soni

Chandan Kumar Mallappa Aladahalli

Bryon Philip Pigg

Ronald A Stone

Justin Wright

Sean P OConnor

Axel Crasemann

Sean P O'Connor

Alex Southern

Vikas Shivaprabhu

Suzanne Kafantaris

Ann Trumble

Kristin Anderson

John Carpenter

Stephanie Wirtz

Jenica Welch

Pauline Moran

Audrey Clarke

Display screen portion with icon

Interruptions of user activity can cost time and money for organizations. Improved technology is needed for interruption and resumption management. A communication engine can monitor the task state of a user, receive an incoming matter, analyze the incoming matter with a classification model, compare the analyzed incoming matter with the priority of the task state of the user, and interrupt the user by providing the incoming matter to the user if the priority of the analyzed incoming matter is greater than the priority of the task state of the user. When the user would like to resume their interrupted task, the communication engine can provide an output refocus communication and automatically return the device software status to the task state of the user when the interruption occurred.

Systems and methods for improved interruption and resumption management

The current disclosure provides methods and systems for visualizing, comparing, and navigating through, labeled image sequences. In one example, a degree of variation between a plurality of labels for an image in a sequence of images may be encoded as a comparison metric, and the comparison metric for each image may be graphed as a function of image position in the sequence of images, thereby providing a contextually rich view of label variation as a function of progression through the sequence of images. Further, the encoded variation of image labels may be used to automatically flag inconsistently labeled images, wherein the flagged images may be highlighted in a graphical user interface presented to a user, pruned from a training dataset, or a loss associated with the flagged image may be scaled based on the encoded variation during training of a machine learning model.

System and methods for visualizing variations in labeled image sequences for development of machine learning models

Various methods and systems are provided for computer-aided diagnosis. In one embodiment, a method comprises acquiring, with an imaging system, a medical image of a subject, generating, with a radiologist model associated with a radiologist of an institution, a computer-aided diagnosis for the medical image, the radiologist model comprising a deep neural network trained on a plurality of diagnoses provided by the radiologist, displaying, to the radiologist via a display device, the medical image and the computer-aided diagnosis, and selectively updating, based on the medical image, one or more of the radiologist model, an institution model associated with the institution, and a geographic model associated with a geographic area containing the institution. In this way, a radiologist may be assisted by a deep neural network model configured as a digital twin of the radiologist.

Methods and systems for computer-aided diagnosis with deep learning models

Systems and techniques that facilitate an augmented inspector interface with targeted, context-driven algorithms are provided. In various embodiments, a magnification component can magnify a portion of a medical image. In various embodiments, a recognition component can recognize an anatomical structure depicted in the portion of the medical image. In various embodiments, a recommendation component can recommend one or more sets of computing algorithms or computing operations related to the anatomical structure. In various embodiments, a menu component can display the one or more recommended sets of computing algorithms or computing operations in a drop-down menu.

Augmented inspector interface with targeted, context-driven algorithms

Display screen or portion thereof with a graphical user interface

A digital pathology system for recording and displaying field of view portions that have been viewed in the context of developing a diagnosis for a pathology case. A whole slide image is presented with shading overlaid on the image illustrating those portions that have been viewed. Portions of the WSI that have received the pathologist's attention at different magnifications are marked with distinguishing hatching.

Field of view navigation tracking

Display screen or portion thereof with graphical user interface

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Pittsburgh, PA, United States of America

James Gualtieri