Abstract: A method is disclosed which includes: receiving, from a third-party content provider, a link to a structured document on an external server, where the link is associated with a post of the online social network by the third-party content provider, and retrieving the structured document from the external server, where the structured document includes content and input fields. This step is followed by extracting a predefined set of information from the structured document about the content and input fields of the structured document. The extracted set of information from the structured document are then evaluated to determine one or more feature-values of the structured document. A machine learning model is used to classify the structured document as an external lead-generation type page based on the one or more feature-values of the structured document and a notification is sent to the third-party content provider if the structured document is classified as an external lead-generation type page.

Abstract: A method is disclosed which includes: receiving, from a third-party content provider, a link to a structured document on an external server, where the link is associated with a post of the online social network by the third-party content provider, and retrieving the structured document from the external server, where the structured document includes content and input fields.. This step is followed by extracting a predefined set of information from the structured document about the content and input fields of the structured document. The extracted set of information from the structured document are then evaluated to determine one or more feature-values of the structured document. A machine learning model is used to classify the structured document as an external lead-generation type page based on the one or more feature-values of the structured document and a notification is sent to the third-party content provider if the structured document is classified as an external lead-generation type page.

Abstract: A method for performing pharmacokinetic analysis in magnetic resonance (MR) images includes administering a dose of contrast agent (CA) into a subject. A sequence of medical images is acquired of the subject at set temporal intervals. The time-based behavior of concentrations of CA is described within the subject for each voxel of each medical image of the sequence of medical images based on a reference voxel using a compartmental model for pharmacokinetic analysis that is based on a set of compartmental model parameters. The compartmental model is solved for each of the compartmental model parameters. The solution for the compartmental model parameters is used to estimate one or more parameters of physiological significance.

Abstract: A method for pharmacokinetic analysis, including: receiving time-series medical image data of a patient introduced with a contrast agent; identifying a reference region in the medical image data; identifying a plurality of points of interest in the medical image data; measuring an intensity of voxels in the reference region; and for each point of interest, measure an intensity of voxels therein, use the measured reference region and point of interest intensities to obtain an expression relating the point of interest's voxel concentration to that of the reference region, wherein the expression is a five-parameter nonlinear model with no reference to an arterial input function; and obtain values for each of the five-parameters by solving the expression and use the obtained values to determine whether the point of interest is malignant.

Abstract: A method for performing pharmacokinetic analysis in magnetic resonance (MR) images includes administering a dose of contrast agent (CA) into a subject. A sequence of medical images is acquired of the subject at set temporal intervals. The time-based behavior of concentrations of CA is described within the subject for each voxel of each medical image of the sequence of medical images based on a reference voxel using a compartmental model for pharmacokinetic analysis that is based on a set of compartmental model parameters. The compartmental model is solved for each of the compartmental model parameters. The solution for the compartmental model parameters is used to estimate one or more parameters of physiological significance.

Abstract: A method for pharmacokinetic analysis, including: receiving time-series medical image data of a patient introduced with a contrast agent; identifying a reference region in the medical image data; identifying a plurality of points of interest in the medical image data; measuring an intensity of voxels in the reference region; and for each point of interest, measure an intensity of voxels therein, use the measured reference region and point of interest intensities to obtain an expression relating the point of interest's voxel concentration to that of the reference region, wherein the expression is a five-parameter nonlinear model with no reference to an arterial input function; and obtain values for each of the five-parameters by solving the expression and use the obtained values to determine whether the point of interest is malignant.