International Business Machines Corporation

Merative US L.p.

Joy Tzung-yu Wu

Tanveer Fathima Syeda-Mahmood

Hongzhi Wang

Alexandros Karargyris

Ashutosh Jadhav

Yaniv Gur

Andrea Giovannini

Chun Lok Wong

Mehdi Moradi

Anup Pillai

Satyananda Kashyap

Marina Bendersky

Vaishnavi Subramanian

Mechanisms are provided to implement a visual analytics pipeline. The mechanisms generate, from an input database of records, a chronology-aware graph data structure of a plurality of records based features specified in an ontology data structure. The chronology-aware graph data structure has vertices representing one or more of events or records based features corresponding to events, and edges representing chronological relationships between events. The mechanisms execute a chronology-aware graph query on the chronology-aware graph data structure to generate a filtered set of vertices and corresponding features corresponding to criteria of the chronology-aware graph query. The mechanisms execute a pattern discovery operation on the filtered set of vertices and corresponding features to identify a subset of vertices and corresponding features that correspond to a relatively higher frequency set of patterns of event paths, and generate a visual analytics graphical representation for the subset of vertices and corresponding features.

Scalable visual analytics pipeline for large datasets

Systems and methods for developing a classification model for classifying medical reports, such as radiology reports. One method includes selecting, from a corpus of reports, a training set and a testing set, assigning labels of a modality and an anatomical focus to the reports in both sets, and extracting a sparse representation matrix for each set based on features in the training set. The method also includes learning, with one or more electronic processors, a correlation between the features of the training set and the corresponding labels using a machine learning classifier, thereby building a classification model and testing the classification model on the reports in the testing set for accuracy using the sparse representation matrix of the testing set. The method further includes predicting, with the classification model, labels of an anatomical focus and a modality for remaining reports in the corpus not included in the sets.

Method and apparatus for selecting radiology reports for image labeling by modality and anatomical region of interest

Embodiments of the present disclosure include a computer-implemented method, a system, and a computer program product for integrating bottom-up segmentation techniques into a semi-supervised image segmentation machine learning model. The computer implemented method includes training a machine learning model with a labeled dataset. The labeled dataset includes ground truth segmentation labels for each sample in the labeled dataset. The computer implemented method also includes generating a pseudo labeled dataset by applying an unlabeled dataset to the machine learning model using a top-down segmentation grouping rule. The computer implemented method further includes evaluating the pseudo labeled dataset using a bottom-up segmentation grouping rule to produce evaluation results, combining the pseudo labeled dataset with the second pseudo labeled dataset into a training dataset, and then retraining the machine learning model with the training dataset.

Integrated bottom-up segmentation for semi-supervised image segmentation

Mechanisms are provided for automatically annotating input images with bounding region annotations and corresponding anomaly labels. The mechanisms segment an input image to generate a mask corresponding to recognized internal structures of a subject. A template data structure is generated that specifies standardized internal structure zones of the subject. The mechanisms register the mask with the template data structure to generate a template registered mask identifying standardized internal structure zones present within the mask, and generate bounding region annotations for each standardized internal structure zone of the template registered mask. The bounding region annotations are correlated with labels indicating whether or not the bounding region comprises an anomaly in the input image based on an analysis of a received natural language text description of the input image. The bounding region annotations and labels are stored in association with the input image.

Automatic bounding region annotation for localization of abnormalities

A system for automated detection and type classification of central venous catheters. The system includes an electronic processor that is configured to, based on an image, generate a segmentation of a potential central venous catheter using a segmentation method and extract, from the segmentation, one or more image features associated with the potential central venous catheter. The electronic processor is also configured to, based on the one or more image features, determine, using a first classifier, whether the image includes a central venous catheters and determine, using a second classifier, a type of central venous catheter included in the image.

Automated detection and type classification of central venous catheters

Mechanisms are provided to implement an automated medical imaging report generator which receives an input medical image and inputs the input medical image into a machine learning (ML) computer model trained to predict finding labels based on patterns of image features extracted from the medical image. The ML computer model generates a prediction of a finding label applicable to the input medical image in terms of a finding label prediction output vector. Based on the finding label prediction output vector, a lookup operation is performed, in a medical report database of previously processed medical imaging report data structures, to find a matching medical imaging report data structure corresponding to the finding label. An output medical imaging report is generated for the input medical image based on natural language content of the matching medical imaging report data structure.

Growing community of inventors

San Jose, CA, United States of America

Joy Tzung-yu Wu