Optimizing specificity under perfect sensitivity for medical data classification

One of the main purposes of a computer-aided diagnosis (CAD) system is to reduce the workload of the radiologists in identifying potential diseases. However, such system can become unreliable and useless if it produces even only a small amount of false negatives, since a misclassification of any unhealthy patient as healthy can result in the delay of treatment, which can lead to fatal outcomes. Designing a CAD system that is capable of reducing the workload of radiologists and meanwhile avoiding any false negative is a very challenging problem. To tackle this problem, we propose a two-stage framework and a novel evaluation criterion, namely optimal specificity under perfect sensitivity (OSPS). We argue that for medical data classification, this criterion is more suitable than other conventional measures such as accuracy, f-score, or area-under-ROC curve. We further propose two learning strategies to improve OSPS. The first aims particularly at multi-instance learning tasks via disregarding the misclassified negative instances of positive patients. The second tries to improve OSPS by embedding more restricted constraints for negatives.