Abstract: Methods and systems are disclosed to aid in the detection of cancer or lesion in a mammogram images. Two mammogram images are input into an application that aids in determining the probability of a cancer or lesion being present in one or both of the images. The images are divided into different nodes and labels are applied to the nodes. The first node is compared to different variants of corresponding nodes on the second image as well as neighboring nodes on the first image. Based upon the comparisons, a unary and binary potential is calculated for the label that is applied to the node. The process is repeated for every possible label and for every node. Once the unary and binary potentials have been calculated, the potentials are input into a Conditional Random Field model to determine the probability of cancer for each node of the images.

Abstract: Methods and systems are disclosed to aid in the detection of areas of interest in an image. Multiple image recognition processes analyze the image and identify areas of interest. The identified areas of interest are compared to determine confidence values for each identified area of interest using a voting process. The confidence values may be used in determining areas of increased interest which are highlighted on the image. In embodiments, identified areas of interest meeting a certain threshold requirement are selected as areas of increased interest. In other embodiments, new areas of increased interest are created by combining areas of interest. Embodiments of the disclosed methods and system may be used to aid in the detection of cancer in mammogram images.