Heat maps: an aid for data analysis and understanding of ROC CAD experiments.

Rationale And Objectives: To demonstrate the value of a new data visualization and exploration method for mutlireader-multicase receiver operating characteristic (MRMC-ROC) experiments of computer-aided detection (CAD) algorithms that uses three-dimensional (3D) heat maps tool adapted from gene expression array analysis.

Materials And Methods: We are using data from a clinical trial of a commercial CAD system for lung cancer detection (RapidScreen RS-2000, Riverain Medical Group, Miamisburg, OH, and Rockville, MD). 3D heat maps, originally developed for displaying changes in gene expression after cancer chemotherapy in MATLAB, were modified to display the radiologists confidence levels as they interpreted chest radiographs and used to visualize the radiologists confidence levels before and after the provision of a CAD system.

Reader variability: what we can learn from computer-aided detection experiments.

Radiologists interpreting images vary in their identification of disease. When 1 radiologist reinterprets the same case differently, it is called intraobserver variability; when 2 radiologists differ with each other on a case, it is called interobserver variability. Computer-aided detection (CAD) systems can increase the detection of disease.

Steepest changes of a probability-based cost function for delineation of mammographic masses: a validation study.

Our purpose in this work was to develop an automatic boundary detection method for mammographic masses and to rigorously test this method via statistical analysis. The segmentation method utilized a steepest change analysis technique for determining the mass boundaries based on a composed probability density cost function. Previous investigators have shown that this function can be utilized to determine the border of the mass body.