1. A method of reviewing medical images and clinical data to generate a diagnosis or treatment decision, comprising: receiving, at a computer-aided detection (CAD) system, the medical images and clinical data; processing, by the CAD system, the medical images and clinical data; to generate initial finding candidates; clustering, by the CAD system, the initial finding candidates into a plurality of groups; classifying, by the CAD system, the initial finding candidates into one or more categories using machine learning algorithms integrated into the CAD system and combined classifiers; assessing, by the CAD system, one or more categories of the initial finding candidates using type 2 fuzzy logic; determining, by the CAD system, detection and assessment statistics based on at least the assessed categories and classified findings using Bayesian probability analysis; modifying, by the CAD system, the classified findings and assessed categories based on additional interactive input; and generating the diagnosis or treatment decision based on the determined detection, assessment statistics, and the additional interactive input.
1. A method of reviewing medical images and clinical data to generate a diagnosis or treatment decision, comprising: receiving, at a computer-aided detection (CAD) system, the medical images and clinical data; processing, by the CAD system, the medical images and clinical data; to generate initial finding candidates; clustering, by the CAD system, the initial finding candidates into a plurality of groups; classifying, by the CAD system, the initial finding candidates into one or more categories using machine learning algorithms integrated into the CAD system and combined classifiers; assessing, by the CAD system, one or more categories of the initial finding candidates using type 2 fuzzy logic; determining, by the CAD system, detection and assessment statistics based on at least the assessed categories and classified findings using Bayesian probability analysis; modifying, by the CAD system, the classified findings and assessed categories based on additional interactive input; and generating the diagnosis or treatment decision based on the determined detection, assessment statistics, and the additional interactive input. 5. The method of claim 1 , wherein classifying the initial finding candidates comprises: extracting selected features from the initial finding candidates; performing, by the CAD system, a first classification using a first neural network trained by a first algorithm; performing, by the CAD system, a second classification using a kernel-based method to map feature vectors of the extracted features into higher-dimensional space; performing, by the CAD system, a third classification using a second neural network trained by a second algorithm; combining the first, second, and third classifications; and determining, by the CAD system, a minimum for each classification, a mean for each classification, and a maximum for each classification.