Classification of small lesions on dynamic breast MRI: Integrating dimension reduction and out-of-sample extension into CADx methodology

Artificial Intelligence in Medicine
Mahesh B NagarajanAxel Wismüller

Abstract

While dimension reduction has been previously explored in computer aided diagnosis (CADx) as an alternative to feature selection, previous implementations of its integration into CADx do not ensure strict separation between training and test data required for the machine learning task. This compromises the integrity of the independent test set, which serves as the basis for evaluating classifier performance. We propose, implement and evaluate an improved CADx methodology where strict separation is maintained. This is achieved by subjecting the training data alone to dimension reduction; the test data is subsequently processed with out-of-sample extension methods. Our approach is demonstrated in the research context of classifying small diagnostically challenging lesions annotated on dynamic breast magnetic resonance imaging (MRI) studies. The lesions were dynamically characterized through topological feature vectors derived from Minkowski functionals. These feature vectors were then subject to dimension reduction with different linear and non-linear algorithms applied in conjunction with out-of-sample extension techniques. This was followed by classification through supervised learning with support vector regression. Area under...Continue Reading

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Nov 14, 2013·Journal of medical and biological engineering·Mahesh B NagarajanAxel Wismüller

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Citations

Jul 18, 2015·IEEE Transactions on Medical Imaging·Shoshana B GinsburgAnant Madabhushi
Jan 3, 2019·AJR. American Journal of Roentgenology·Marina CodariRubina Manuela Trimboli
Mar 28, 2019·Journal of Magnetic Resonance Imaging : JMRI·Peter GibbsElizabeth J Sutton

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