12. A program storage device readable by a computer, tangibly embodying a program of instructions executable by the computer to perform the method steps for computer aided detection of anatomical abnormalities in medical images, said method comprising the steps of: providing a plurality of abnormality candidates and features of said abnormality candidates; and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(w T x+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more computationally complex features are used for each successive stage of said cascade of classifiers, wherein each stage of said cascade solves a linear program formulated using a training error rate ξ equivalent to max {0,1−y(w T x+b)} and an l 1 -norm equivalent to ∥w∥ t =Σ|w 1 | summed over all features, wherein said linear program is equivalent to the system represented by min u , v , ξ ⁢ λ ⁢ ∑ j = 1 d ⁢ ( u j + v j ) + μ l + ⁢ ∑ i ∈ C + ⁢ ξ i + 1 - μ l - ⁢ ∑ i ∈ C - ⁢ ξ i , ⁢ y ( ∑ j ⁢ X ij ⁡ ( u j - v j ) + b ) + ξ i ≥ 1 , ⁢ such ⁢ ⁢ that ⁢ ⁢ ξ i ≥ 0 , i = 1 , … ⁢ , l , ⁢ u j , v j ≥ 0 , j = 1 , … ⁢ , d , ( 1 ) wherein λ>0 is a regularization parameter, {x i , y i }, i=1, . . . , l denotes the abnormality candidates, y denotes a label indicating whether or not a candidate associated with a feature vector is a true positive, X denotes a feature matrix of d features wherein each row represents candidate feature vector x, and each column specifies a feature, l + is the number of positive candidates and l − the number of negative candidates, C + and C − contain, respectively, the sets of indices of positive candidates and negative candidates, 0≦μ≦1 is a tuning parameter for combining the false negative rate and false positive rate, and w j =u j −v j .
12. A program storage device readable by a computer, tangibly embodying a program of instructions executable by the computer to perform the method steps for computer aided detection of anatomical abnormalities in medical images, said method comprising the steps of: providing a plurality of abnormality candidates and features of said abnormality candidates; and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(w T x+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more computationally complex features are used for each successive stage of said cascade of classifiers, wherein each stage of said cascade solves a linear program formulated using a training error rate ξ equivalent to max {0,1−y(w T x+b)} and an l 1 -norm equivalent to ∥w∥ t =Σ|w 1 | summed over all features, wherein said linear program is equivalent to the system represented by min u , v , ξ ⁢ λ ⁢ ∑ j = 1 d ⁢ ( u j + v j ) + μ l + ⁢ ∑ i ∈ C + ⁢ ξ i + 1 - μ l - ⁢ ∑ i ∈ C - ⁢ ξ i , ⁢ y ( ∑ j ⁢ X ij ⁡ ( u j - v j ) + b ) + ξ i ≥ 1 , ⁢ such ⁢ ⁢ that ⁢ ⁢ ξ i ≥ 0 , i = 1 , … ⁢ , l , ⁢ u j , v j ≥ 0 , j = 1 , … ⁢ , d , ( 1 ) wherein λ>0 is a regularization parameter, {x i , y i }, i=1, . . . , l denotes the abnormality candidates, y denotes a label indicating whether or not a candidate associated with a feature vector is a true positive, X denotes a feature matrix of d features wherein each row represents candidate feature vector x, and each column specifies a feature, l + is the number of positive candidates and l − the number of negative candidates, C + and C − contain, respectively, the sets of indices of positive candidates and negative candidates, 0≦μ≦1 is a tuning parameter for combining the false negative rate and false positive rate, and w j =u j −v j . 17. The computer readable program storage device of claim 12 , wherein each classifier of said cascade of classifiers uses an accumulative set of features that includes those features used by preceding classifiers in said cascade.