Multiview CAD in TMI

Maurice Samulski

IEEE TMIMy paper entitled “Optimizing case-based detection performance in a multi-view CAD system for mammography” has been accepted for publishing in IEEE Transactions on Medical Imaging and you can get it from here. It is my second publication, and I’m proud that it is going to appear in the highly ranked journal TMI. This article describes our approach to improve the clinically important case-based detection performance detecting masses in mammograms using a multi-view classifier with an alternative learning rule. Read further for the technical abstract.

When reading mammograms, radiologists combine information from multiple views to detect abnormalities. Most computer-aided detection (CAD) systems, however, use primitive methods for inclusion of multi-view context or analyze each view independently. In previous research it was found that in mammography lesion-based detection performance of CAD systems can be improved when correspondences between MLO and CC views are taken into account. However, detection at case level detection did not improve.

In this paper, we propose a new learning method for multi-view CAD systems, which is aimed at optimizing case-based detection performance. The method builds on a single-view lesion detection system and a correspondence classifier. The latter provides class probabilities for the various types of region pairs and correspondence features. The correspondence classifier output is used to bias the selection of training patterns for a multi-view CAD system. In this way training can be forced to focus on optimization of case-based detection performance. The method is applied to the problem of detecting malignant masses and architectural distortions.

Experiments involve 454 mammograms consisting of 4 views with a malignant region visible in at least one of the views. To evaluate performance, 5-fold cross validation and FROC analysis was performed. Bootstrapping was used for statistical analysis.

A significant increase of case-based detection performance was found when the proposed method was used. Mean sensitivity increased by 4.7% in the range of 0.01-0.5 false positives per image.

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