Multiple instance learning for breast MRI based on generic spatio-temporal features
In this paper we investigate multiple instance learning (MIL), using generic tile-based spatio-temporal features, for the classification of benign and malignant lesions in breast cancer magnetic resonance imaging (MRI). In particular, we compare the performance of citation-kNN (CkNN) and conventional kNN against a traditional approach based on bespoke features extracted from a segmented region-of-interest (ROI). Results demonstrate that tile-based CkNN has equivalent performance to ROI-based classification. However, the tile-based approach does not require any domain specific features typically used in breast MRI. This not only has the potential to make tile-based classification robust to inaccuracies in the delineation of suspicious lesions, but also makes it suitable for the detection of suspicious lesions prior to segmentation.