International Business Machines Corporation

Pol MacAonghusa

Stefano Braghin

Sergiy Zhuk

Spyridon Antonatos

Naoise Holohan

Rahul Nair

Jonathan P Epperlein

Seshu Tirupathi

Tigran Tchrakian

Stephen Michael Moore

Michele Gazzetti

Mykhaylo Zayats

Techniques that facilitate three-dimensional (3D) delineation of tumor boundaries via one or more supervised machine learning algorithms are provided. An example embodiment includes a computer-implemented method that includes: extracting, by a computing system operatively coupled to a processor, one or more feature vectors from a time-series evolution of fluorescence distribution observed at a section of bodily tissue of interest, wherein the one or more feature vectors represent a physical model describing on-tissue dye dynamics of the section of bodily tissue; and generating, by the computing system, a classification attribute for the section of bodily tissue represented by the one or more feature vectors, wherein a pre-trained classifier designates the section of bodily tissue as a biopsy or a non-biopsy candidate through execution of the one or more supervised machine learning algorithms.

Three-dimensional delineation of tumor boundaries via supervised machine learning algorithms and augmented reality

In an approach for classifying regions of tissue captured in multispectral videos into medically meaningful classes using GPU accelerated perfusion estimation, a processor receives one or more multispectral videos of a subject tissue of a patient. A processor extracts one or more fluorescence time series profiles from the one or more multispectral videos. A processor estimates one or more sets of perfusion parameters based on the one or more fluorescence time series profiles. A processor inputs one or more feature vectors into a classifier, wherein the one or more feature vectors are derived the one or more sets of perfusion parameters. A processor receives a classification result for each of the one or more feature vectors, wherein the classification result comprises a set of medically relevant labels for each of the one or more feature vectors with a level of certainty for each label of the set of medically relevant labels.

GPU accelerated perfusion estimation from multispectral videos

Embodiments for performing consistent data masking in a distributed computing environment by a processor. A dictionary based data masking operation is performed on one or more datasets with causal ordering of the one or more datasets to enable reconstruction of a state of the one or more dictionaries for the one or more datasets.

Providing consistent data masking using causal ordering

Embodiments for implementing intelligent classification of region of interest in an organism in a computing environment by a processor. Time series data of a contrast agent in one or more regions of interest captured from multispectral image streams may be collected. The one or more regions of interest may be classified into one of a plurality of classes by applying one or more perfusion models, representing spatio-temporal behavior of the contrast agent reflected by the time series data, and by using a machine learning operation.

Intelligent classification of regions of interest of an organism from multispectral video streams using perfusion models

Various embodiments are provided for linking of anonymized datasets in a computing environment are provided. A number of linking records may be identified between an anonymized dataset and one or more non-anonymized datasets of a knowledge base according to one or more equivalence classes and a generalization level.

Fast linking of anonymized datasets

Embodiments for watermarking anonymized datasets using decoys in a computing environment are provided. One or more decoy records may be embedded in an anonymized dataset such that a re-identification attack on the anonymized dataset targets the one or more decoy records.

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Carbury, Ireland

Pol MacAonghusa