International Business Machines Corporation

Ahmed El Harouni

Tanveer Fathima Syeda-Mahmood

David James Beymer

Yu Long Cao

Mehdi Moradi

Ehsan Dehghan Marvast

Simona Cohen

Deepika Kakrania

Amram Abutbul

Yaniv Gur

Satyananda Kashyap

Aly Megahed

Hongzhi Wang

Mohamed Mohamed

Arkadiusz Sitek

Alexandros Karargyris

Amin Katouzian

Samir Tata

Yiting Xie

Benedikt Werner Graf

Gopalkrishna Veni

Hui Tang

Prasanth Prasanna

Arun Krishnan

Girish Narayan

King Chung Ho

Vicky Guo

Mechanisms are provided to implement a multi-layer analytics framework. The multi-layer analytics framework obtains a plurality of analytics from one or more analytics source computing systems, at least two analytics being written in different computer programming languages. The multi-layer analytics framework applies a wrapper to each of the analytics in the plurality of analytics to thereby generate wrapped analytics. The wrapper provides a unified interface for executing the analytics in the plurality of analytics regardless of the particular computer programming language used to create the analytics. The multi-layer analytics framework registers the wrapped analytics in an analytics registry, and executes an analytics pipeline comprising wrapped analytics in the analytics registry to perform an analytics operation based on the unified interface of the wrappers of the wrapped analytics.

Analytics framework for selection and execution of analytics in a distributed environment

A multi-layer analytics framework is provided that obtains a plurality of analytics from one or more analytics source computing systems. The framework applies a wrapper to each of the analytics, where the wrapper provides a unified interface for executing the analytics regardless of the particular computer programming language used to create the analytics. The framework registers the wrapped analytics in an analytics registry, receives a request to perform an analytics operation on an input dataset, from a request computing system, and automatically generates an analytics pipeline comprising a plurality of wrapped analytics retrieved from the analytics registry. The framework executes the analytics pipeline and returns results of executing the analytics pipeline to the requestor computing system.

Registration, composition, and execution of analytics in a distributed environment

Techniques for fracture detection are provided. A first image is received to be processed to identify rib fractures. A first set of regions of interest (ROIs) is identified by processing the first image using a first machine learning model, where each ROI in the first set of ROIs corresponds to a first potential fracture. Further, a first ROI of the first set of ROIs is upsampled, and the system attempts to verify the first potential fracture in the first ROI by processing the upsampled first ROI using a second machine learning model.

Automated fracture detection using machine learning models

A computer-implemented method according to one embodiment includes identifying a plurality of policies to be implemented within a system, aggregating the plurality of policies to create an aggregated policy, disseminating the aggregated policy within the system, receiving data collected according to the aggregated policy, and disaggregating the data.

Systemic adaptive data management in an internet of things environment

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement an intelligent medical image viewing engine. The intelligent medical image viewing engine receives a medical imaging study data structure comprising a plurality of electronic medical images from a medical image database. An image processing component executing within the intelligent medical image viewing engine analyzes the medical imaging study data structure to identify, for each electronic medical image in the plurality of electronic medical images, a corresponding set of image attributes. The intelligent medical image viewing engine receives a user input specifying at least one filter attribute for generating a medical image output and correlates the at least one filter attribute with at least one medical attribute of medical images to be used for selection of electronic medical images from the medical imaging study data structure. An image selection component executing within the intelligent medical image viewing engine selects a subset of the electronic medical images in the plurality of electronic medical images based on the correlation of the at least one filter attribute with the at least one medical attribute. A user interface generation component executing within the intelligent medical image viewing engine generates and outputs a medical image output comprising the subset of electronic medical images based on the selection.

Intelligently organizing displays of medical imaging content for rapid browsing and report creation

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a multi-modal classification and segmentation engine for anatomical segmentation identifying modes and viewpoints in biomedical images. The mechanism trains a neural network perform simultaneous classification and segmentation using a set of training images. The neural network provides a classification output that identifies a class label and a second output that identifies a segmentation label. The multi-modal classification and segmentation engine provides a biomedical image as the input image to the neural network. The neural network outputs a plurality of class label probabilities for a plurality of class labels and a plurality of segmentation label probabilities for each of a plurality of segmentation labels. A post-processing component executing within the multi-modal classification and segmentation engine classifies the biomedical image as an identified modality and an identified viewpoint based on the plurality of class label probabilities. The multi-modal classification and segmentation engine segments the biomedical image based on the plurality of segmentation label probabilities. The multi-modal classification and segmentation engine outputs the classified and segmented biomedical image.

Anatomical segmentation identifying modes and viewpoints with deep learning across modalities

A method comprises (a) collecting (i) a set of chest computed tomography angiography (CTA) images scanned in the axial view and (ii) a manual segmentation of the images, for each one of multiple organs; (b) preprocessing the images such that they share the same field of view (FOV); (c) using both the images and their manual segmentation to train a supervised deep learning segmentation network, wherein loss is determined from a multi-dice score that is the summation of the dice scores for all the multiple organs, each dice score being computed as the similarity between the manual segmentation and the output of the network for one of the organs; (d) testing a given (input) pre-processed image on the trained network, thereby obtaining segmented output of the given image; and (e) smoothing the segmented output of the given image.

System for segmentation of anatomical structures in cardiac CTA using fully convolutional neural networks

Automatic detection of disease from analysis of echocardiographer findings in echocardiogram videos is provided. In various embodiments, a plurality of medical images containing embedded text are read. The plurality of medical images are clustered into a plurality of clusters. Medical images of a first cluster of the plurality of clusters are ranked based on the frequency of measurement names within the medical images of the first cluster. A candidate tabular template is generated indicative of a layout of measurement name/value pairs within the medical images of the first cluster. According to the candidate tabular template, optical character recognition is applied to one of the plurality of medical images of the first cluster to extract candidate measurements. The candidate tabular template and the candidate measurements are presented to a user.

Automatic detection of disease from analysis of echocardiographer findings in echocardiogram videos

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a medical imaging story board creation engine. The medical imaging story board creation engine executing in the data processing system receives a patient data structure comprising a medical imaging study comprising a plurality of electronic medical images. The medical imaging story board creation engine analyzes the patient data structure to determine a modality of the medical imaging study. The medical imaging story board creation engine determines, based on the determined modality of the medical imaging study, for each electronic image in the medical imaging study, at least one of an image mode or viewpoint. The medical imaging story board creation engine performs a saliency feature extraction operation on the electronic medical images in the medical imaging study based on the image mode or viewpoint of each of the electronic medical images. The medical imaging story board creation engine selects a subset of electronic medical images based on results of the saliency feature extraction. The medical imaging story board creation engine generates and outputs a collection of selected electronic medical images based on the selection of the subset of electronic medical images to form a medical imaging story board.

Automatic creation of imaging story boards from medical imaging studies

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San Jose, CA, United States of America

Ahmed El Harouni