Qualia Computing, Inc.

Icad, Inc.

United States of America As Represented by the Secretary of the Air Force

Quale LLC

Steven Keith Rogers

Randy P Broussard

John E Rosenstengel

Edward M Ochoa

Philip Amburn

Martin P DeSimio

Telford S Berkey

Jeffrey W Hoffmeister

Thomas F Rathbun

Michael J Collins

Richard A Mitchell

Thomas P Rathbun

Matthew Kabrisky

Adam Steven Rogers

Mark Edward Oxley

Metin N Gurcan

Thomas E Dube

Maha Sallam

W Scott Parr

Claudia V Kropas-Hughes

Richard A Raines

Steven W Worrell

Russell C Hardie

Kenneth W Bauer

A process for recommending choices to a user uses a first model representing the user's subconscious and a second model representing the user's consciousness. The process comprises receiving data and performing, using the second model, a simulation by modeling the user and a third party to derive a simulation result that determines how the user would react to a situation based at least in part on the received data. Using the first model and the simulation, a first set of recommendation choices based at least in part on the received data is derived, and a set of recommendation choices for presentation based on the first set of recommendation choices is determined and presented to the user. As the user is making a selection, the user is monitored for conscious decisions and subconscious reactions, which are used to update the first model and the second model.

Two-model recommender

A method for recommending choices to a user comprises receiving data. A first set of recommendation choices based on the data is derived using a first model that models a user's subconscious reactions (i.e., intuition) to the data, and a second set of recommendation choices based on the data is derived using a second model that models the user's consciousness. The first and second sets of recommendation choices are blended using a blending function based on characteristics of a domain of the data to create a third set of recommendation choices that are arranged in an order, which is presented to the user. A conscious decision of the user and subconscious reactions of the user are monitored, and data representing the subconscious reactions of the user and data representing the conscious decisions of the user are appended to a first and second memory space, respectively.

A method, apparatus and program product are provided to recognize malware in a computing environment having at least one computer. A sample is received. An automatic determination is made by the at least one computer to determine if the sample is malware using static analysis methods. If the static analysis methods determine the sample is malware, dynamic analysis methods are used by the at least one computer to automatically determine if the sample is malware. If the dynamic analysis methods determine the sample is malware, the sample is presented to a malware analyst to adjudicate the automatic determinations of the static and dynamic analysis. If the adjudication determines the sample is malware, a response action is initiated to recover from or mitigate a threat of the sample.

Malware target recognition

A method for refining shape estimates of detected abnormalities in medical images, particularly for the detection of pulmonary lesions in CT imagery is described. A cue point is refined prior to an initially segmentation of the lesion. A radial gradient is computed for points on an initial segmented lesion. Portions of the lesion with radial gradients deviating beyond a threshold angle are removed from the lesion. Registering imagery from more than one CT exam uses a high intensity structure, essentially bone, to provide coarse and fine alignment of a set of two-dimensional images is also described. A MIP image is formed from the three-dimensional images. A second MIP image is then formed from imagery. The second MIP image is correlated with the first MIP image to determine a preferred registration location.

Shape estimates and temporal registration of lesions and nodules

The present invention provides for the use of computer-aided detection (CAD) system output displays for providing accurate representations of areas for subsequent exams. Since the CAD output, unlike the original medical imagery, is not used during the initial reading, the radiologist does not mark it until a final determination is reached regarding subsequent procedures. Additionally, since the CAD output contains versions of the original imagery, the regions indicated by the radiologist are shown in the context of the particular anatomical detail for a given patient. This detail assists the technologist, other physicians and patients in more efficiently and accurately locating the exact area for subsequent exams.

Use of computer-aided detection system outputs in clinical practice

The present invention provides for the use of computer-aided detection (CAD) system output displays for providing accurate representations of areas for subsequent exams. Since the CAD output, unlike the original medical imagery, is not used during the initial reading, the radiologist does not mark it until a final determination is reached regarding subsequent procedures. Additionally, since the CAD output contains versions of the original imagery, the regions indicated by the radiologist are shown in the context of the particular anatomical detail for the given patient. This detail assists the technologist in more efficiently and accurately locating the exact area for subsequent exams.

A computer aided detection method and system to assist radiologists in the reading of medical images. The method and system has particular application to the area of mammography including detection of clustered microcalcifications and densities. A microcalcification detector is provided wherein individual detections are rank ordered and classified, and one of the features for classification is derived using a multilayer perceptron. A density detector is provided including an iterative, dynamic region growing module with embedded subsystem for rank ordering and classification of a best subset of candidate masks. A post processing stage is provided where detections are analyzed in the context of a set of images for a patient. Three analysis methods are used to distribute a limited number of detections across the image set and further within each image, and additionally to perform a normalcy classification. The normalcy classification is used to remove all detections from an image set when predetermined normalcy conditions are met. The final output of the system is a set of indications overlaid on the input medical images.

Computer aided detection of masses and clustered microcalcifications with single and multiple input image context classification strategies

A method and system for filtering messages where the importance of a message is determined by analyzing the message body in conjunction with message attributes. Message body refers to the text in the body of the message, whereas message attributes convey information about the message. In another embodiment, analysis of the user's current computing environment provides additional input to the filtering system. This allows for preferentially weighting messages of user's current interests. Analysis includes computation of feature vectors and subsequent input to a discriminant function. The discriminant function provides a test statistic which is compared to a threshold. If the test statistic exceeds the threshold, the incoming message is passed by the filtering system and may be displayed to the user. In another embodiment, message body and attributes are used to anticipate significant events in a time series, such as streaming financial data.

Message and user attributes in a message filtering method and system

A computer aided detection method and system to assist radiologists in the reading of medical images. The method and system has particular application to the area of mammography including detection of clustered microcalcifications and densities. A microcalcification detector is provided wherein individual detections are rank ordered and classified, and one of the features for classification is derived using a multilayer perceptron. A density detector is provided including an iterative, dynamic region growing module with embedded subsystem for rank ordering and classification of a best subset of candidate masks. A post processing stage is provided where detections are analyzed in the context of a set of images for a patient. The post processing includes a normalcy classification including providing computed values corresponding to each detection from a category of detections on an image set, computing a normalcy value using the computed values, and removing all detections from an image set when the normalcy value does not meet a predetermined condition. The final output of the system is a set of indications overlaid on the input medical images.

Method for analyzing detections in a set of digital images using case based normalcy classification

A computer aided detection method and system to assist radiologists in the reading of medical images. The method and system has particular application to the area of mammography including detection of clustered microcalcifications and densities. A microcalcification detector is provided wherein individual detections are rank ordered and classified, and one of the features for classification is derived using a multilayer perceptron. A density detector is provided including an iterative, dynamic region growing module with embedded subsystem for rank ordering and classification of a best subset of candidate masks. Features are computed from a detection on an input image by providing first and second regions on the input image corresponding to areas inside and outside the detection, measurements are computed based on values derived from the two regions, a standard deviation is computed for the measurements in each region, and a feature for the detection is computed using a Bauer-Fisher ratio. A post processing stage is provided where detections are analyzed in the context of a set of images for a patient. The final output of the system is a set of indications overlaid on the input medical images.

Method for determining features from detections in a digital image using a bauer-fisher ratio

A method and system for detecting and displaying clustered microcalcifications in a digital mammogram, wherein a single digital mammogram is first automatically cropped to a breast area sub-image which is then processed by means of an optimized Difference of Gaussians filter to enhance the appearance of potential microcalcifications in the sub-image. The potential microcalcifications are thresholded, clusters are detected, features are computed for the detected clusters, and the clusters are classified as either suspicious or not suspicious by means of a neural network. Thresholding is preferably by sloping local thresholding, but may also be performed by global and dual-local thresholding. The locations in the original digital mammogram of the suspicious detected clustered microcalcifications are indicated. Parameters for use in the detection and thresholding portions of the system are computer-optimized by means of a genetic algorithm. The results of the system are optimally combined with a radiologist's observation of the original mammogram by combining the observations with the results, after the radiologist has first accepted or rejected individual detections reported by the system.

Method for combining automated detections from medical images with observed detections of a human interpreter

An efficient neural network computing technique capable of synthesizing two sets of output signal data from a single input signal data set. The method and device of the invention involves a unique integration of autoassociative and heteroassociative neural network mappings, the autoassociative neural network mapping enabling a quality metric for assessing the generalization or prediction accuracy of the heteroassociative neural network mapping.

Autoassociative-heteroassociative neural network

Joint optimization of parameters for the detection of clustered microcalcifications in digital mammograms

A method and system for detecting and displaying clustered microcalcifications in a digital mammogram, wherein a single digital mammogram is first automatically cropped to a breast area sub-image which is then processed by means of an optimized Difference of Gaussians filter to enhance the appearance of potential microcalcifications in the sub-image. The potential microcalcifications are thresholded clusters are detected, features are computed for the detected clusters, and the clusters are classified as either suspicious or not suspicious by means of a neural network. Thresholding is preferably by sloping local thresholding but may also be performed by global and dual-local thresholding. The locations in the original digital mammogram of the suspicious detected clustered microcalcifications are indicated. Parameters for use in the detection and thresholding portions of the system are computer-optimized by means of a genetic algorithm. The results of the system are optimally combined with a radiologist's observation of the original mammogram by combining the observations with the results, after the radiologist has first accepted or rejected individual detections reported by the system.

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Beavercreek, OH, United States of America

Steven Keith Rogers