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9 March 2010 Selective reduction of CAD false-positive findings
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Computer-Aided Detection (CAD) systems are becoming widespread supporting tools to radiologists' diagnosis, especially in screening contexts. However, a large amount of false positive (FP) alarms would inevitably lead both to an undesired possible increase in time for diagnosis, and to a reduction in radiologists' confidence in CAD as a useful tool. Most CAD systems implement as final step of the analysis a classifier which assigns a score to each entry of a list of findings; by thresholding this score it is possible to define the system performance on an annotated validation dataset in terms of a FROC curve (sensitivity vs. FP per scan). To use a CAD as a supportive tool for most clinical activities, an operative point has to be chosen on the system FROC curve, according to the obvious criterion of keeping the sensitivity as high as possible, while maintaining the number of FP alarms still acceptable. The strategy proposed in this study is to choose an operative point with high sensitivity on the CAD FROC curve, then to implement in cascade a further classification step, constituted by a smarter classifier. The key issue of this approach is that the smarter classifier is actually a meta-classifier of more then one decision system, each specialized in rejecting a particular type of FP findings generated by the CAD. The application of this approach to a dataset of 16 lung CT scans previously processed by the VBNACAD system is presented. The lung CT VBNACAD performance of 87.1% sensitivity to juxtapleural nodules with 18.5 FP per scan is improved up to 10.1 FP per scan while maintaining the same value of sensitivity. This work has been carried out in the framework of the MAGIC-V collaboration.
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N. Camarlinghi, I. Gori, A. Retico, and F. Bagagli "Selective reduction of CAD false-positive findings", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 762430 (9 March 2010);

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