Ge Precision Healthcare LLC

General Electric Company

Baker Hughes Oilfield Operations LLC

Vikram Reddy Melapudi

Krishna Seetharam Shriram

Chandan Kumar Mallappa Aladahalli

Rakesh Mullick

Rahul Venkataramani

Bipul Das

Gopal Biligeri Avinash

Ravi Soni

Tao Tan

Chandan Kumar Aladahalli

Máté Fejes

Aparna Chakrapani Sheila-Vadde

Sohan Rashmi Ranjan

Prafull Sharma

Pál Tegzes

Manoj Kumar Koyithitta Meethal

Lehel Ferenczi

Pavan Annangi

Utkarsh Agrawal

Zita Herczeg

Prasad Sudhakara Murthy

Barbara Darázs

Dániel Attila Szabó

Suma Memana Narayana Bhat

Christian Fritz Perrey

Abhijit Vishwas Patil

Megha Navalgund

Prasad Sudhakar

László Ruskó

Satya Mohan Vamsi Andalam

Rohan Keshav Patil

Utkarsh Agarwal

Manikandasriram Srinivasan Ramanagopal

Shital Sheshrao Yelne

Systems/techniques that facilitate machine learning image analysis based on explicit equipment parameters are provided. In various embodiments, a system can access a medical image generated by a medical imaging device. In various instances, the system can perform, via execution of a machine learning model, an inferencing task on the medical image. In various cases, the machine learning model can receive as input the medical image and a set of equipment parameters. In various aspects, the set of equipment parameters can indicate how the medical imaging device was configured to generate the medical image.

Machine learning image analysis based on explicit equipment parameters

Systems/techniques that facilitate variable confidence machine learning are provided. In various embodiments, a system can access a medical image. In various aspects, the system can perform, via execution of a machine learning model, a regression task on the medical image, wherein the machine learning model can receive as input both the medical image and a user-specified confidence indicator.

Variable confidence machine learning

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

Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.

Multimodality image processing techniques for training image data generation and usage thereof for developing mono-modality image inferencing models

Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.

System and methods for inferring thickness of object classes of interest in two-dimensional medical images using deep neural networks

Various methods and systems are provided for segmenting a three-dimensional (3D) region of interest (ROI) from an image sequence. In one embodiment, a method includes receiving a positive point selection or negative point selection for an ROI in a first image slice of an image sequence, mapping the first image slice and the positive point selection or negative point selection to a first segmentation mask of the ROI using a weakly supervised segmentation model, simulating positive and negative point selections in a second image slice based on the first segmentation mask to produce a plurality of simulated positive and negative point selections, wherein the second image slice is adjacent to the first image slice, and mapping the second image slice and the plurality of simulated positive and negative point selections to a second segmentation mask for the ROI in the second image slice using the weakly supervised segmentation model.

Methods and systems for fast three-dimensional image segmentation and annotation by imitating weak supervision

Methods and systems are provided for generating ultrasound probe motion recommendations. In one example, a method includes obtaining an ultrasound image of a source scan plane, the ultrasound image acquired with an ultrasound probe at a first location relative to a patient, entering the ultrasound image as input to a probe recommendation model trained to output a set of recommendations to move the ultrasound probe from the first location to a plurality of additional locations at which a plurality of target scan planes can be imaged, and displaying the set of recommendations on a display device.

Medical imaging method

Systems, computer-implemented methods, and computer program products that facilitate temporalizing and/or spatializing a machine learning and/or artificial intelligence network are provided. In various embodiments, a processor can combine output data from different layers of an artificial neural network trained on static image data. In various embodiments, the processor can employ the artificial neural network to infer an outcome from an image instance in a sequence of images based on combined output data from the different layers of the artificial neural network.

Temporalizing or spatializing networks

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

A magneto-optic imaging apparatus adapted for use with an imaging device, such as a smartphone, tablet computer, or borescope, for visually detecting defects in the surface or subsurface of a component using a magneto-optic effect. The magneto-optic imaging apparatus includes a housing with an interface for connection with the imaging device such that a camera and light source of the imaging device may be implemented for magneto-optic visualization. The housing includes optical elements including one or more filters, a polarizer, a magneto-optic or garnet film, and an analyzer. The housing also includes a flexible coil or electrical terminals for inducing a magnetic field in the component. The magneto-optic imaging apparatus may include a translucent or transparent body of conformable material or materials to conform the magneto-optic film and flexible coil to a non-planar surface of the component.

Apparatus and method for visualizing defects using a magneto-optical effect

Systems, machine-readable media, and methods for ultrasound imaging can include acquiring three-dimensional data for one or more patient data sets and generating a three-dimensional environment based on one or more transition areas identified between a plurality of volumes of the three-dimensional data. A method can also include generating a set of probe guidance instructions based at least in part on the one or more transition areas and the plurality of volumes of the three-dimensional data, and acquiring, using an ultrasound probe, a first frame of two-dimensional data for a patient. The method can also include executing the set of probe guidance instructions to provide probe feedback for acquiring at least a second frame of two-dimensional data.

Systems and methods for generating ultrasound probe guidance instructions

Systems, methods and computer program products are provided to collect ultrasound (US) data. A processor is configured to acquire the US data along one or more acquisition scan planes. The US data defines a plurality of image frames that have a first image quality. The processor is further configured to apply a generative model to at least one of the US data or plurality of image frames to generate a synthetic scan plane image along a synthetic scan plane. The generative model is defined based on one or more training ultrasound data sets. The synthetic scan plane image has an image quality that is common with the first image quality of the plurality of image frames. The system further comprises a display configured to display the synthetic scan plane image.

Method and system for managing image quality utilizing a generative model

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

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.

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Bengaluru, India

Vikram Reddy Melapudi