Abstract: 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.

Abstract: Embodiments are provided for identification of a section of bodily tissue as either a candidate or a non-candidate for pathology tests. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include a feature composition component that generates a feature vector representing a physical model describing dye dynamics that determines a group of multispectral images of a section of bodily tissue. The computer-executable components also can include a classification component that generates a classification attribute for the section of bodily tissue by applying a classification model to the feature vector. The classification attribute designates the section of bodily tissue as one of biopsy-candidate or non-biopsy-candidate.

Abstract: Embodiments for implementing intelligent boundary delineation of a region of interest of an organism in two spatial dimensions 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. One or more regions of interest having one or more perfusion patterns may be identified from the time series data. Boundaries of the one or more regions of interest may be delineated into at least two spatial dimensions, wherein the boundaries of the one or more regions of interest include one or more selected labels.

Abstract: 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.

Abstract: Embodiments for implementing intelligent boundary delineation of a region of interest of an organism in two spatial dimensions in a computing environment by a processor. Time series data of a contrast agent in one or more regions of interest captured from multi spectral image streams may be collected. One or more regions of interest having one or more perfusion patterns may be identified from the time series data. Boundaries of the one or more regions of interest may be delineated into at least two spatial dimensions, wherein the boundaries of the one or more regions of interest include one or more selected labels.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments for data anonymity by a processor. A dataset may be transformed into an anonymous dataset by applying a differential privacy operation and a clustering operation to the dataset.

Abstract: 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.

Abstract: Embodiments for performing self-contained, consistent data masking in a distributed computing environment by a processor. A data masking operation is performed on one or more datasets in one of a plurality of data formats such that a key of each value of each key-value pair representing a common set of columns or paths for the one or more datasets is masked.

Abstract: Techniques facilitating automatically detecting unauthorized use of sensitive information in content communicated over a network are provided. A computer-implemented method can comprise receiving, by a system operatively coupled to a processor, from a first entity, data associated with the first entity and one or more rules defining use of the data by a second entity. The data and the one or more rules can be defined by the first entity. The computer-implemented method can also comprise analyzing, by the system, content communicated over a network by the second entity to determine whether the content violates the one or more rules. The computer-implemented method can further comprise generating, by the system, information indicative of one or more violations of the one or more rules based on a determination that the content violates the one or more rules.

Abstract: 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.

Abstract: Embodiments for data anonymity by a processor. A dataset may be transformed into an anonymous dataset by applying a differential privacy operation and a clustering operation to the dataset.

Abstract: Embodiments for performing self-contained, consistent data masking in a distributed computing environment by a processor. A data masking operation is performed on one or more datasets in one of a plurality of data formats such that a key of each value of each key-value pair representing a common set of columns or paths for the one or more datasets is masked.

Abstract: A method for a vulnerability analysis application is described. The method includes assembling a profile from a first vulnerability factor grouping from plurality of vulnerability factors with each vulnerability factor having a vulnerability factor value. The method also includes performing a probabilistic operation on the vulnerability factor values from the first vulnerability factor grouping to obtain a first probabilistic result. The method also includes performing a dynamic operation on the first probabilistic result from the probabilistic operation to obtain a first time to vulnerable state for the profile. The method also includes displaying the first time to vulnerable state for the profile.