Ge Precision Healthcare LLC

Intel Corporation

Sarit Shwartz

Dani Pinkovich

Noa Alkobi

Christian Fritz Perrey

Ziv Aviv

Jeffrey Danowitz

David Stanhill

Antonio Fabian Fermoso

Walter Duda, Jr

Robert John Anderson

Systems and methods are provided for enhanced heart medical imaging operations, particularly as by incorporating use of artificial intelligence (AI) based fetal heart functional analysis and/or real-time and automatic ejection fraction (EF) measurement and analysis.

Methods and systems for medical imaging based analysis of ejection fraction and fetal heart functions

Methods and systems are provided for augmenting ultrasound image training data, which may be used to train one or more machine learning models. One example method for augmenting ultrasound training data comprises, selecting an ultrasound image and a ground truth output corresponding to the ultrasound image, determining a first modification to apply to the ultrasound image, applying the first modification to the ultrasound image to produce an augmented ultrasound image, modifying the ground truth output based on the first modification to produce an augmented ground truth output corresponding to the augmented ultrasound image, and training a machine learning model using the augmented ultrasound image and the augmented ground truth output. In this way, a machine learning model may learn a more robust mapping from ultrasound image features to expected output, with less probability of overfitting, and with increased generalizability to noisy ultrasound images, or ultrasound images containing artifacts.

Systems and methods for generating augmented training data for machine learning models

An apparatus may include an image sensor that contains a multiplicity of pixel elements to detect one or more images and a processor circuit coupled to the image sensor. The apparatus may include a white balance module for execution on the processor circuit to receive, based upon a detected image of the one or more images, for a plurality of pixel elements of the multiplicity of pixel elements, three of more gray level values for a respective three or more color channels, to determine grayness likelihood functions for the respective three or more color channels, the three or more grayness likelihood functions comprising a proportional contribution to grey pixels of the detected image from one or more gray levels for each respective color channel, and to determine a white balance gain for two or more color channels based upon the determined grayness likelihood functions. Other embodiments are described and claimed.

Apparatus and techniques for image processing

Growing community of inventors

Haifa, Israel

Sarit Shwartz