Computerized detection and classification schemes have the potential of increasing diagnostic accuracy in medical imaging by alerting radiologists to lesions that they initially overlooked and/or assisting in the classification of detected lesions. These schemes, generally referred to as computer-aided diagnosis (CAD) schemes, typically employ multiple parameters such as threshold values or filter weights to arrive at a detection or classification decision. In order for the system to have a high performance, the values of these parameters need to be set optimally. Conventional optimization techniques are designed to optimize a scalar objective function. The task of optimizing the performance of a CAD scheme, however, is clearly a multiobjective problem: we wish to simultaneously improve the sensitivity and reduce the false-positive rate of the system. In this work we investigate a multiobjective approach optimizing CAD schemes. In a multiobjective optimization, multiple objectives are simultaneously optimized, with the objective now being a vector-valued function. The multiobjective optimization problem admits a set of solutions, known as the Pareto-optimal set, which are equivalent in the absence of any information regarding the preferences of the objectives. The performances of the Pareto-optimal solutions can be interpreted as operating points on an optimal ROC or FROC curve, greater than or equal to the points on any possible ROC or FROC curve for a given dataset and given CAD classifier.