Classification of Hepatic Lesions using the Matching Metric
This work addresses liver lesion classification for medical imaging, but it is incremental as it applies existing topological methods to a specific domain.
The paper tackled the problem of classifying liver lesions by using topological features from persistent homology and a support vector machine, achieving results on a dataset of 132 annotated lesions and showing that two-dimensional persistent homology outperforms one-dimensional in this application.
In this paper we present a methodology of classifying hepatic (liver) lesions using multidimensional persistent homology, the matching metric (also called the bottleneck distance), and a support vector machine. We present our classification results on a dataset of 132 lesions that have been outlined and annotated by radiologists. We find that topological features are useful in the classification of hepatic lesions. We also find that two-dimensional persistent homology outperforms one-dimensional persistent homology in this application.