Abstract: A method for data-driven professional network expansion techniques is implemented via a computing system including a processor. The method includes executing, via a network, an enterprise application on a remote computing system operated by a user associated with an enterprise and surfacing a professional networking UI on a display of the remote computing system during execution of the enterprise application. The method includes accessing enterprise-level data corresponding to the user and the enterprise, extracting parameters associated with the user and the enterprise from the enterprise-level data, and automatically generating, based on the extracted parameters, the user's professional network and a list of suggested professional contacts.

Abstract: The present invention provides a method for enhancing microcalcifications for computer-aided lesion detection, review and diagnosis. The method includes two steps: partitioning of the breast tissue area and filtering with a convolution kernel. The partitioning process delineates: breast glandular tissue area; fat tissue sub-area and dense tissue sub-area. The 2D or 3D convolution kernels are designed to highlight small spot regions of rapid intensity changes on 2D mammograms or 3D tomosynthesis mammography images. The size of such a kernel is calculated based on the resolution of the mammographic images that are produced from each manufacturer's digital radiography device.

Abstract: A method for image linear structure detection in medical imaging. The method includes locating microcalcification (mcc) candidate spots in a mammographic image; forming candidate clusters; assigning ranks to the candidate clusters; identifying linear structures in the neighborhood where the candidate clusters reside; and altering the ranks of the candidate clusters for which linear structures have been identified in the neighborhood.

Abstract: The present invention provides a method and system using computer-aided detection (CAD) algorithms to aid diagnosis and visualization of tomosynthesis mammography data. The proposed CAD algorithms process two-dimensional and three-dimensional tomosynthesis mammography images and identify regions of interest in breasts. The CAD algorithms include the steps of preprocessing; candidate detection of potential regions of interest; and classification of each region of interest to aid reading by radiologists. The detection of potential regions of interest utilizes two dimensional projection images for generating candidates. The resultant candidates in two dimensional images are back-projected into the three dimensional volume images. The feature extraction for classification operates in the three dimensional image in the neighborhood of the back-projected candidate location.

Abstract: A computer-aided diagnostic method and system provide image annotation information that can include an assessment of the probability, likelihood or predictive value of detected or identified suspected abnormalities as an additional aid to the radiologist. More specifically, probability values, in numerical form and/or analog form, are added to the locational markers of the detected and suspected abnormalities. The task of a physician using such a system as disclosed is believed to be made easier by displaying markers representing different regions of interest.

Abstract: This invention provides a computational intelligence method and system that can be used interactively by a radiologist in a “concurrent read” model to aid diagnosis from medical images. In particular, the invention operates more like a very patient, indefatigable knowledge accumulating and communicating companion for the radiologist, rather than a second “expert” whose advice is sought after a normal review. The system works interactively with the radiologist during image reading, prompting areas to review in more detail, providing computer generated features and interpretation, and suggesting potential diagnoses for areas of suspicion that are identified either by the machine or the human. In addition, the human can obtain more information from the system—the radiologist can query as to why a particular region is highlighted, or why a particular diagnosis is postulated for an area.

Abstract: A method and system for Computer Aided Detection (CAD) of abnormalities in X-ray images in which the features and/or criteria used in the CAD analysis are displayed as coded descriptors on the digital image of the X-ray to provide an indication to a user of the basis underlying the identification of a particular abnormality therefore increasing the reliability of a diagnosis established by the user.

Abstract: A method and apparatus are disclosed for an image preprocessing device that automatically detects chestwall laterality; removes border artifacts; and segments breast tissue and pectoral muscle from digital mammograms. The algorithms in the preprocessing device utilize the computer cache, a vertical Sobel filter and a probabilistic Hough transform to detect curved edges. The preprocessing result, along with a pseudo-modality normalized image, can be used as input to a CAD (computer-aided detection) server or to a mammography image review workstation. In the case of workstation input, the preprocessing results improve the protocol for chestwall-to-chestwall image hanging, and support optimal image contrast display of each segmented region.

Abstract: This invention provides a simple user interface and sequencing viewing method for a mammography interpretation workstation. In particular, the invention includes the method and apparatus that moves the source images and the associated data using two-level of pre-fetching and caching mechanism, sequences the reading workflow (including worklist, hanging protocol, viewing sequencing), draws markup using electronic grease pan, and automatically generates the recall forms and screening reports. The user interface operates on single button and mouse wheel style to maximize the radiologists' efficiency.

Abstract: This invention utilizes a number of Computational Intelligence (CI) techniques with different learning methods in a computer-aided detection, review and diagnosis (CAD) device. Specifically, an unsupervised learning method is used for clustering of types of abnormal findings. Then a number of classifiers for each type of findings are trained with appropriate learning algorithms; and combined in three different manners to produce one classifier that can be operated at three different operating points. A fuzzy system is used for mapping the findings to diagnostic reports constructed using a formal language. Finally, the finding statistics is calculated based on Bayesian probability. During image review, the device provides the readers some insight as to how it derives its outputs. The output of the device can be updated in an interactive and progressive manner by a human reader (radiologist). The output from classification can be updated by the human, and is fed as input to the assessment task.

Abstract: A method for image linear structure detection in medical imaging. The method includes locating microcalcification (mcc) candidate spots in a mammographic image; forming candidate clusters; assigning ranks to the candidate clusters; identifying linear structures in the neighborhood where the candidate clusters reside; and altering the ranks of the candidate clusters for which linear structures have been identified in the neighborhood.

Abstract: A computer-aided diagnostic method and system provide image annotation information that can include an assessment of the probability, likelihood or predictive value of detected or identified suspected abnormalities as an additional aid to the radiologist. More specifically, probability values, in numerical form and/or analog form, are added to the locational markers of the detected and suspected abnormalities. The task of a physician using such a system as disclosed is believed to be made easier by displaying markers representing different regions of interest.

Abstract: A computer-aided diagnostic method and system provide image annotation information that can include an assessment of the probability, likelihood or predictive value of detected or identified suspected abnormalities as an additional aid to the radiologist. More specifically, probability values, in numerical form and/or analog form, are added to the locational markers of the detected and suspected abnormalities. The task of a physician using such a system as disclosed is believed to be made easier by displaying markers representing different regions of interest.

Abstract: A computer-aided diagnostic method and system provide image annotation information that can include an assessment of the probability, likelihood or predictive value of detected or identified suspected abnormalities as an additional aid to the radiologist. More specifically, probability values, in numerical form and/or analog form, are added to the locational markers of the detected and suspected abnormalities. The task of a physician using such a system as disclosed is believed to be made easier by displaying markers representing different regions of interest.

Abstract: A computer aided detection (CAD) system for assigning CAD applications to digital images. The system comprises a case input system (CIS), image data repositories and an algorithm server. There is also provided a method for assigning CAD applications to digital images the method comprising the steps of selecting an acquisition model (AM) and selecting a CAD application using the selected AM.

Abstract: A computer-aided diagnostic method and system provide image annotation information that can include an assessment of the probability, likelihood or predictive value of detected or identified suspected abnormalities as an additional aid to the radiologist. More specifically, probability values, in numerical form and/or analog form, are added to the locational markers of the detected and suspected abnormalities. The task of a physician using such a system as disclosed is believed to be made easier by displaying markers representing different regions of interest.

Abstract: A computer-aided diagnostic method and system provide image annotation information that can include an assessment of the probability, likelihood or predictive value of detected or identified suspected abnormalities as an additional aid to the radiologist. More specifically, probability values, in numerical form and/or analog form, are added to the locational markers of the detected and suspected abnormalities. The task of a physician using such a system as disclosed is believed to be made easier by displaying markers representing different regions of interest.