Patent ID: 7962428

Claim:
A computer-implemented method for training classifiers for Computer-Aided Detection in medical images, said method performed by a computer comprising the steps of: providing an image feature training set {(x i , y i )} i=1 l , wherein x i εR d are input feature variables and y i ε{−1,1} are class labels for labeling each variable and a cascade of K classifiers to be trained; minimizing, for each classifier k, a first cost function Φ ⁡ ( α k ) + ∑ i = 1 I ⁢ w i × max ⁡ ( 0 , 1 - α T ⁢ y i ⁢ x i k ) , to initialize an α k 0 associated with each classifier k, wherein the function Φ: R (d) = R is a regularization function and {w i : w i ≧0, ∀i} is a pre-determined weight associated with x i ; for each classifier k, fixing all classifiers except classifier k and minimizing a second cost function Φ k ⁡ ( α k ) + v 1 ⁢ ∑ i ∈ C - ⁢ ⁢ w i × max ⁡ ( 0 , e ik ) + v 2 ⁢ ∑ i ∈ C + ⁢ ⁢ max ⁡ ( 0 , e i ⁢ ⁢ 1 , … ⁢ , e ik , … ⁢ , e iK ) to solve for α k c for a counter value c using the training dataset {(x i k , y i )} i=1 l , wherein w i = ∏ m = 1 , m ≠ k K ⁢ max ⁡ ( 0 , e im ) , e ik =1−α k T y i x ik ′ defines a hinge loss of the i th training example {(x ik ′, y i )} induced by classifier k, v 1 and v 2 are weighting factors, C + and C − are corresponding sets of indices for positive and negative classes respectively, and wherein x ik ′ denotes the subset of features in x i used by classifier k; calculating ⁢ ⁢ J c ⁡ ( α 1 c , … ⁢ , α K c ) = ∑ k = 1 K ⁢ Φ k ⁡ ( α k c ) + v 1 ⁢ ∑ i ∈ C - ⁢ ∏ k = 1 K ⁢ max ⁡ ( 0 , e ik ) + v 2 ⁢ ∑ i ∈ C + ⁢ max ⁡ ( 0 , e i ⁢ ⁢ 1 , … ⁢ , e iK ) for each classifier k; and comparing J c with a previous iteration J c−1 , wherein if J c −J c−1 is less than a predetermined tolerance, said classifier training is completed.