Abstract: A computer implemented method and a system choosing optimal disease treatment among several possible treatment options for a patient are provided. The system computes cancer-free survival rates for each considered treatment based on predicting recurrence rate of a disease and/or cancer outcome for a particular patient. The treatment survival models use quantitative data from histopathological images of the patient, clinical data and other patient information. The system segments the histopathological images into biologically meaningful components; automatically determines disease-affected regions in one or more of the segmented image components. The system also partitions the disease-affected regions in each image into a number clusters. Those that are determined to be the most associated with the disease outcome are used as a source of the imaging information for the survival modeling.

Abstract: The present disclosure describes systems and methods for authorization. The method may include accessing, by an authorization engine for a transaction by a user, an activity pattern model of the user from a database. The activity pattern model of the user may be indicative of a geospatial behavior of the user over time. The authorization engine may determine a set of sensors available for facilitating the transaction, each of the sensors assigned with a usability value prior to the transaction. The authorization engine may access an activity pattern model of the sensors, the activity pattern model of the sensors indicative of geospatial characteristics of one or more of the sensors over time. The authorization engine may determine a convenience metric for each of a plurality of subsets of the sensors, using the activity pattern model of the user, the activity pattern model of the sensors, and usability values of corresponding sensors.

Abstract: The present disclosure describes systems and methods of using iris data for authentication. A biometric encoder may translate an image of the iris into a rectangular representation of the iris. The rectangular representation may include a plurality of rows corresponding to a plurality of annular portions of the iris. The biometric encoder may extract an intensity profile from at least one of the plurality of rows, the intensity profile modeled as a stochastic process. The biometric encoder may obtain a stationary stochastic component of the intensity profile by removing a non-stationary stochastic component from the intensity profile. The biometric encoder may remove at least a noise component from the stationary component using auto-regressive based modeling, to produce at least a non-linear background signal, and may combine the non-stationary component and the at least the non-linear background signal, to produce a biometric template for authenticating the person.

Abstract: The present disclosure describes systems and methods of using iris data for authentication. A biometric encoder may translate an image of the iris into a rectangular representation of the iris. The rectangular representation may include a plurality of rows corresponding to a plurality of annular portions of the iris. The biometric encoder may extract an intensity profile from at least one of the plurality of rows, the intensity profile modeled as a stochastic process. The biometric encoder may obtain a stationary stochastic component of the intensity profile by removing a non-stationary stochastic component from the intensity profile. The biometric encoder may remove at least a noise component from the stationary component using auto-regressive based modeling, to produce at least a non-linear background signal, and may combine the non-stationary component and the at least the non-linear background signal, to produce a biometric template for authenticating the person.

Abstract: The present disclosure describes systems and methods for authorization. The method may include accessing, by an authorization engine for a transaction by a user, an activity pattern model of the user from a database. The activity pattern model of the user may be indicative of a geospatial behavior of the user over time. The authorization engine may determine a set of sensors available for facilitating the transaction, each of the sensors assigned with a usability value prior to the transaction. The authorization engine may access an activity pattern model of the sensors, the activity pattern model of the sensors indicative of geospatial characteristics of one or more of the sensors over time. The authorization engine may determine a convenience metric for each of a plurality of subsets of the sensors, using the activity pattern model of the user, the activity pattern model of the sensors, and usability values of corresponding sensors.

Abstract: Systems and methods are provided for automated diagnosis and grading of tissue images based on morphometric data extracted from the images by a computer. The morphometric data may include image-level morphometric data such as fractal dimension data, fractal code data, wavelet data, and/or color channel histogram data. The morphometric data may also include object-level morphometric data such as color, structural, and/or textural properties of segmented image objects (e.g., stroma, nuclei, red blood cells, etc.).

Abstract: Methods and systems are provided that use clinical information, molecular information and computer-generated morphometric information in a predictive model for predicting the occurrence (e.g., recurrence) of a medical condition, for example, cancer. In an embodiment, a model that predicts prostate cancer recurrence is provided, where the model is based on features including one or more (e.g., all) of biopsy Gleason score, seminal vesicle invasion, extracapsular extension, preoperative PSA, dominant prostatectomy Gleason grade, the relative area of AR+ epithelial nuclei, a morphometric measurement of epithelial nuclei, and a morphometric measurement of epithelial cytoplasm. In another embodiment, a model that predicts clinical failure post-prostatectomy is provided, wherein the model is based on features including one or more (e.g.

Abstract: Embodiments of the present invention are directed to quantitative analysis of tissues enabling the measurement of objects and parameters of objects found in images of tissues including perimeter, area, and other metrics of such objects. Measurement results may be input into a relational database where they can be statistically analyzed and compared across studies. The measurement results may be used to create a pathological tissue map of a tissue image, to allow a pathologist to determine a pathological condition of the imaged tissue more quickly.

Abstract: Embodiments of the present invention are directed to quantitative analysis of tissues enabling the measurement of objects and parameters of objects found in images of tissues including perimeter, area, and other metrics of such objects. Measurement results may be input into a relational database where they can be statistically analyzed and compared across studies. The measurement results may be used to create a pathological tissue map of a tissue image, to allow a pathologist to determine a pathological condition of the imaged tissue more quickly.

Abstract: Methods and systems are provided that use clinical information, molecular information and computer-generated morphometric information in a predictive model for predicting the occurrence (e.g., recurrence) of a medical condition, for example, cancer.

Abstract: Methods and systems are provided that use clinical information, molecular information and computer-generated morphometric information in a predictive model for predicting the occurrence (e.g., recurrence) of a medical condition, for example, cancer. In an embodiment, a model that predicts prostate cancer recurrence is provided, where the model is based on features including seminal vesicle involvement, surgical margin involvement, lymph node status, androgen receptor (AR) staining index of tumor, a morphometric measurement of epithelial nuclei, and at least one morphometric measurement of stroma.

Abstract: Methods and systems are provided that use clinical information, molecular information and computer-generated morphometric information in a predictive model for predicting the occurrence (e.g., recurrence) of a medical condition, for example, cancer. In an embodiment, a model that predicts prostate cancer recurrence is provided, where the model is based on features including seminal vesicle involvement, surgical margin involvement, lymph node status, androgen receptor (AR) staining index of tumor, a morphometric measurement of epithelial nuclei, and at least one morphometric measurement of stroma.

Abstract: Systems and methods are provided for automated diagnosis and grading of tissue images based on morphometric data extracted from the images by a computer. The morphometric data may include image-level morphometric data such as fractal dimension data, fractal code data, wavelet data, and/or color channel histogram data. The morphometric data may also include object-level morphometric data such as color, structural, and/or textural properties of segmented image objects (e.g., stroma, nuclei, red blood cells, etc.).

Abstract: Methods and systems are provided that use clinical information, molecular information and computer-generated morphometric information in a predictive model for predicting the occurrence (e.g., recurrence) of a medical condition, for example, cancer.

Abstract: Embodiments of the present invention are directed to quantitative analysis of tissues enabling the measurement of objects and parameters of objects found in images of tissues including perimeter, area, and other metrics of such objects. Measurement results may be input into a relational database where they can be statistically analyzed and compared across studies. The measurement results may be used to create a pathological tissue map of a tissue image, to allow a pathologist to determine a pathological condition of the imaged tissue more quickly.