General Electric Company

Ge Precision Healthcare LLC

Hammersmith Imanet Limited

Rakesh Mullick

Krishna Seetharam Shriram

Dattesh Dayanand Shanbhag

Bipul Das

Sheshadri Thiruvenkadam

Srikanth Suryanarayanan

Gopal Biligeri Avinash

Vikram Reddy Melapudi

Tao Tan

Yogisha Mallya

Vidya Pundalik Kamath

Ravi Soni

Deepa Anand

Vivek Prabhakar Vaidya

Sandeep Suryanarayana Kaushik

Sohan Rashmi Ranjan

Máté Fejes

Mahendra Madhukar Patil

Florian Wiesinger

Chitresh Bhushan

Shekhar Dwivedi

Pál Tegzes

Amit Kumar Singh

Kris Filip Johan Jules Thielemans

Chandan Kumar Mallappa Aladahalli

Arathi Sreekumari

Sudhanya Chatterjee

Lehel Ferenczi

Utkarsh Agrawal

László Ruskó

Zita Herczeg

Chandan Kumar Aladahalli

Fei Zhao

Barbara Darázs

Soma Biswas

Dániel Attila Szabó

Mitali Janardan More

Xiaoxing Li

Christopher Judson Hardy

Thomas Kwok-Fah Foo

Robert F Senzig

Scott David Wollenweber

Ravindra Mohan Manjeshwar

Thomas K F Foo

Floribertus P M Heukensfeldt Jansen

Sandeep Dutta

Christian Fritz Perrey

Sandeep Narendra Gupta

Alexander Ganin

Marc Thomas Edgar

Kevin Matthew Koch

Hariharan Ravishankar

Rahul Venkataramani

André De Almeida Maximo

Evren Asma

Ajit Shankaranarayanan

James Gualtieri

Daniel J Blezek

Prasad Sudhakar

Hua Qian

Sai Hareesh Anamandra

Nitin Singhal

Maud Bonnard

Srikrishnan Viswanathan

Navneeth Subramanian

Lehel Mihály Ferenczi

Balázs Péter Cziria

Uday Damodar Patil

Kumar Rajamani

Pavan Kumar Veerabhadra Annangi

Sumit Nath

Hongxiang Yi

Vanika Singhal

Kunlin Cao

Umesha Perdoor Srinivas Adiga

Suvadip Mukherjee

Anja Brau

Nithin Nagaraj

Utkarsh Agarwal

Borbála Deák-Karancsi

Sanjay Kumar Nt

Suresh Emmanuel Devadoss Joel

Syed Asad Hashmi

Priyavrat Jhunjhunwala

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

Techniques are described for generating high quality training data collections for training artificial intelligence (AI) models in the medical imaging domain. A method embodiment comprises receiving, by a system comprising processor, input indicating a clinical context associated with usage of a medical image dataset, and selecting, by the system, one or more data scrutiny metrics for filtering the medical image dataset based on the clinical context. The method further comprises applying, by the system, one or more image processing functions to the medical image dataset to generate metric values of the one or more data scrutiny metrics for respective medical images included in the medical image dataset, filtering, by the system, the medical image dataset into one or more subsets based on one or more acceptability criteria for the metric values.

Generating high quality training data collections for training artificial intelligence models

Systems and techniques that facilitate data diversity visualization and/or quantification for machine learning models are provided. In various embodiments, a processor can access a first dataset and a second dataset, where a machine learning (ML) model is trained on the first dataset. In various instances, the processor can obtain a first set of latent activations generated by the ML model based on the first dataset, and a second set of latent activations generated by the ML model based on the second dataset. In various aspects, the processor can generate a first set of compressed data points based on the first set of latent activations, and a second set of compressed data points based on the second set of latent activations, via dimensionality reduction. In various instances, a diversity component can compute a diversity score based on the first set of compressed data points and second set of compressed data points.

Data diversity visualization and quantification for machine learning models

Systems/techniques that facilitate deep learning multi-planar reformatting of medical images are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can localize, via execution of a machine learning model, a set of landmarks depicted in the three-dimensional medical image, a set of principal anatomical planes depicted in the three-dimensional medical image, and a set of organs depicted in the three-dimensional medical image. In various instances, the system can determine an anatomical orientation exhibited by the three-dimensional medical image, based on the set of landmarks, the set of principal anatomical planes, or the set of organs. In various cases, the system can rotate the three-dimensional medical image, such that the anatomical orientation now matches a predetermined anatomical orientation.

Deep learning multi-planar reformatting of medical images

The present disclosure relates to use of a workflow for automatic prescription of different radiological imaging scan planes across different anatomies and modalities. The automated prescription of such imaging scan planes helps ensure contiguous visualization of the different landmark structures. Unlike prior approaches, the disclosed technique determines the necessary planes using the localizer images itself and does not explicitly segment or delineate the landmark structures to perform plane prescription.

Plane selection using localizer images

Techniques are described that facilitate dynamic multimodal segmentation selection and fusion in medical imaging. In one example embodiment, a computer processing system receives a segmentation dataset comprising a combination of different image segmentations of an anatomical object of interest respectively segmented via different segmentation models from different medical images captured of the (same) anatomical object, wherein the different medical images and the different image segmentations vary with respect to at least one of, capture modality, acquisition protocol, or acquisition parameters. The system employs a dynamic ranking protocol as opposed to a static ranking protocol to determine ranking scores for the different image segmentations that control relative contributions of the different image segmentations in association with combining the different image segmentations into a fused segmentation for the anatomical object. The system further combines the different image segmentations based on the ranking scores to generate the fused image segmentation.

Dynamic multimodal segmentation selection and fusion

Systems/techniques that facilitate interpretable task-specific dimensionality-reduction are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can generate, via execution of a first deep learning neural network, a voxel-wise weight map corresponding to the three-dimensional medical image and a set of projection vectors corresponding to the three-dimensional medical image. In various instances, the system can generate a set of two-dimensional projection images of the three-dimensional medical image, based on the voxel-wise weight map and the set of projection vectors. In various cases, the first deep learning neural network can be trained in a serial pipeline with a second deep learning neural network that is configured to perform an inferencing task on two-dimensional inputs. This can cause the set of two-dimensional projection images to be tailored to the inferencing task.

Interpretable task-specific dimensionality reduction

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

Techniques are described for generating reformatted views of a three-dimensional (3D) anatomy scan using deep-learning estimated scan prescription masks. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a mask generation component that employs a pre-trained neural network model to generate masks for different anatomical landmarks depicted in one or more calibration images captured of an anatomical region of a patient. The computer executable components further comprise a reformatting component that reformats 3D image data captured of the anatomical region of the patient using the masks to generate different representations of the 3D image data that correspond to the different anatomical landmarks.

Generating reformatted views of a three-dimensional anatomy scan using deep-learning estimated scan prescription masks

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

Techniques are provided for generating enhanced image representations from original X-ray images using deep learning techniques. In one embodiment, a system is provided that includes a memory storing computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a reception component, an analysis component, and an artificial intelligence component. The analysis component analyzes the original X-ray image using an AI-based model with respect to a set of features of interest. The AI component generates a plurality of enhanced image representations. Each enhanced image representation highlights a subset of the features of interest and suppresses remaining features of interest in the set that are external to the subset.

Generating enhanced x-ray images using constituent image

A system is presented. The system includes an acquisition subsystem configured to obtain images corresponding to a target domain. Moreover, the system includes a processing subsystem in operative association with the acquisition subsystem and including a memory augmented domain adaptation platform configured to compute one or more features of an input image corresponding to a target domain, identify a set of support images based on the features of the input image, where the set of support images corresponds to the target domain, augment an input to a machine-learnt model with a set of features, a set of masks, or both corresponding to the set of support images to adapt the machine-learnt model to the target domain, and generate an output based at least on the set of features, the set of masks, or both. Additionally, the system includes an interface unit configured to present the output for analysis.

System and method for memory augmented domain adaptation

Growing community of inventors

Bengaluru, India

Rakesh Mullick