1. A computer-implemented method of training a classifier for computer aided detection of digitized medical images, comprising the steps of: providing a plurality of bags, each bag containing a plurality of feature samples of a single region-of-interest in said medical image, wherein said feature samples include texture, shape, intensity, and contrast of said region-of-interest, wherein each region-of-interest has been labeled as either malignant or healthy; and training said classifier on said plurality of bags of feature samples, subject to the constraint that at least one point in a convex hull of each bag, corresponding to said feature sample, is correctly classified according to the label of the associated region-of-interest, wherein said classifier is trained on a computer, and wherein said classifier is trained by minimizing the expression vE(ξ)+Φ(ω,η)+Ψ(λ) over arguments (ξ,ω,η,λ)εR r+n+1+γ subject to the conditions 
 ξ i =d i −(λ j i B j i ω−eη ), 
 ξεΩ, 
 e′λ j i =1, 
 0≦λ j i , wherein ξ={ξ 1 ,. .. ,ξ r } are slack terms, E:R r R represents a loss function, ω is a hyperplane coefficient, η is the bias term, λ is a vector containing the coefficients of the convex combination that defines the representative point of bag i in class j wherein 0≦λ j i ,e′λ j i =1, γ is the total number of convex hull coefficients corresponding to the representative points in class j,Φ:R (n+1) R is a regularization function on the hyperplane coefficients, Ψ is a regularization function on the convex combination coefficients λ j i , Ω represents a feasible set for ξ matrix B j i εR m j i ×n ,i=1,. .. ,r j , jε{±1} is the i th bag of class label j, r is the total number of representative points, n is the number of features, m j i is the number of rows in B, vector dε{±1} r j represents binary bag-labels for the malignant and healthy sets, respectively, and the vector e represents a vector with all its elements equal to one.