Ge Precision Healthcare LLC

General Electric Company

Chandan Kumar Mallappa Aladahalli

Krishna Seetharam Shriram

Vikram Reddy Melapudi

Rakesh Mullick

Dattesh Dayanand Shanbhag

Jiayu Chen

Rahul Venkataramani

Dirk Ryan Padfield

Sheshadri Thiruvenkadam

Arathi Sreekumari

Paulo Ricardo Mendonca

Vivek Prabhakar Vaidya

Abhijit Vishwas Patil

Sandeep Suryanarayana Kaushik

James Gualtieri

Shubao Liu

Aditi Garg

Shital Sheshrao Yelne

Hidenori Shikata

Theresa Rose Broniak

Systems/techniques that facilitate task-specific image style transfer are provided. In various embodiments, a system can access a first medical image, wherein the first medical image can exhibit anatomical content and a first visual style. In various aspects, the system can generate, via execution of an optimization algorithm, a second medical image based on the first medical image, wherein the second medical image can exhibit the anatomical content and a second visual style that is different from the first visual style. In various instances, the optimization algorithm can be based on feature maps extracted from a pre-trained deep learning neural network that has been configured to perform an inferencing task on medical images exhibiting the second visual style.

Task-specific image style transfer

Techniques are described for maintaining patient privacy in association with obtaining patient data for machine learning applications. In an example, a method can comprise accessing, by a system comprising a processor, a training dataset associated with a machine learning model, the training dataset comprising of data samples respectively comprising unique characteristics of subjects. The method further comprises determining, by the system, characteristic curve information that correlates different data portions extracted from the data samples to respective probabilities of matching the different data portions to respective ones of the data samples from which they are extracted. The method further comprises controlling, by the system, collection of new data portions extracted from new data samples corresponding to the data samples based on the new data portions conforming to criteria that defines a target data portion, wherein the criteria comprise a probability of the respective probabilities that satisfies an anonymity criterion.

Privacy driven data subset sizing

Methods and systems are provided for generating user guidance for ultrasound imaging. In one example, a method includes determining, with a probe recommendation model, a user action to an ultrasound probe prior to and/or during acquisition of a current ultrasound image frame, one or more anatomical features in the current ultrasound image frame, and an anatomy view of the current ultrasound image frame, and outputting, for display on a display device, a probe motion recommendation based on the user action, the one or more anatomical features, and the anatomy view.

System and methods for adaptive guidance for medical imaging

Techniques are described that facilitate training an artificial intelligence (AI) model in a manner that ensures equitable model performance across different sub-groups. According to an embodiment, a system is provided that includes a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components include a training component that trains a machine learning (ML) model on training data to perform an inferencing task using an equitable loss function that drives equitable performance of the ML model across different sub-groups represented by the training data, resulting in trained version of the ML model that provides a defined equitable performance level across the different sub-groups. The equitable loss function is 'sub-group aware' and penalizes variation in model performance across the sub-groups during model training and validation.

Artificial intelligence model training that ensures equitable performance across sub-groups

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

A system and method for automatically adjusting beamformer parameters based on ultrasound image analysis to enhance ultrasound image acquisition is provided. The method includes acquiring, by an ultrasound system, an ultrasound image. The method includes segmenting, by at least one processor, the ultrasound image to identify anatomical structure(s) and/or image artifact(s) in the ultrasound image. The method includes detecting, by the at least one processor, a location of each of the identified anatomical structure(s) and/or image artifact(s). The method includes automatically adjusting, by the at least one processor, at least one beamformer parameter based on the detected location of one or more of the identified anatomical structure(s) and/or the image artifact(s). The method includes acquiring, by the ultrasound system, an enhanced ultrasound image based on the automatically adjusted at least one beamformer parameter. The method includes presenting, at a display system, the enhanced ultrasound image.

Method and system for providing ultrasound image enhancement by automatically adjusting beamformer parameters based on ultrasound image analysis

Systems and methods for guided lung coverage and automated detection using ultrasound devices are disclosed. The method for guided coverage and automated detection of pathologies of a subject includes positioning an ultrasound probe on a region of the subject body to be imaged. The method includes capturing a video of the subject and processing the video to generate a torso image of the subject and identify location of the ultrasound probe on the subject body. The method includes registering the video to an anatomical atlas to generate a mask of the region of the subject body comprising a plurality of sub-regions of the subject body to be imaged and superimposing the mask over the torso image. The method further includes displaying an indicator corresponding to a location of the each of the plurality of the sub-regions on the torso image. The method further includes displaying the relative position of the ultrasound probe with respect to the indicator corresponding to the location of the each of the plurality of plurality of sub-regions of the subject body to be imaged.

Guided lung coverage and automated detection using ultrasound devices

Various methods and systems are provided for generating a context awareness graph for a medical scan image. In one example, the context awareness graph includes relative size and relative position annotations with regard to one or more internal anatomical features in the scan image to enable a user to determine a current scan plane and further, to guide the user to a target scan plane.

Method and systems for context awareness enabled ultrasound scanning

Methods and systems are provided for assessing image quality of ultrasound images. In one example, a method includes determining a probe position quality parameter of an ultrasound image, the probe position quality parameter representative of a level of quality of the ultrasound image with respect to a position of an ultrasound probe used to acquire the ultrasound image, determining one or more acquisition settings quality parameters of the ultrasound image, each acquisition settings quality parameter representative of a respective level of quality of the ultrasound image with respect to a respective acquisition setting used to acquire the ultrasound image, and providing feedback to a user of the ultrasound system based on the probe position quality parameter and/or the one or more acquisition settings quality parameters, the probe position quality parameter and each acquisition settings quality parameter determined based on output from separate image quality assessment models.

Systems and methods for ultrasound image quality determination

Methods and systems are provided for turbulence monitoring during ultrasound scanning. In one example, during scanning with an ultrasound probe, a turbulence amount between two successive frames may be monitored, and in response to the turbulence amount at or above the higher threshold, deployment of the one or more image interpretation protocols may be stopped or delayed until the turbulence amount decreases below the higher threshold.

Methods and systems for turbulence awareness enabled ultrasound scanning

A method for assisted reading of automated ultrasound image volumes includes receiving a plurality of scan images generated from an imaging device, wherein the plurality of scan images comprises a chest wall region. The method further includes determining a chest wall model representative of the chest wall region based on the plurality of scan images. The method also includes determining a plurality of segmented scan images segmented along the chest wall region based on the chest wall model. In addition, the method includes determining lesion information using an automated lesion detection technique applied to the plurality of segmented scan images. The method also includes displaying the plurality of scan images along with at least one of the lesion information and the chest wall model.

System and method for visualization of ultrasound volumes

A method for synchronization of a longitudinal data set from a subject includes receiving a first ensemble registration estimate having a first reference image corresponding to a first image ensemble and receiving a second image ensemble different from the first image ensemble. The method includes determining a second reference image based on the second image ensemble and the first reference image. Further, the method includes determining a second ensemble registration estimate based on the first ensemble registration estimate, the second reference image, the first image ensemble and the second image ensemble using an optimization technique. The method further includes generating a synchronized image ensemble corresponding to the first image ensemble and the second image ensemble based on the second ensemble registration estimate. The method also includes determining a medical condition of the subject by a medical practitioner based on the synchronized image ensemble.

System and method for longitudinal data processing

Systems and methods for determining parameters for image analysis are provided. One method includes obtaining ultrasound data of an object, generating an image of the object, and identifying a region of interest in the image. The method also includes determining a plurality of spatially varying parameters for image analysis of the region of interest using prior information for one or more objects of interest, including prior location information for the one or more objects of interest, and wherein the plurality of spatially varying parameters are determined for a plurality of sections of the region of interest and different for at least some of the plurality of sections. The method further includes using the plurality of spatially varying parameters for performing image analysis of the region of interest in the image to determine the location of the one or more objects of interest.

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Bengaluru, India

Chandan Kumar Mallappa Aladahalli