Subtype classification of malignant lymphoma using immunohistochemical staining pattern

Noriaki Hashimoto, Kaho Ko, Tatsuya Yokota, Kei Kohno, Masato Nakaguro, Shigeo Nakamura, Ichiro Takeuchi, Hidekata Hontani

Abstract: For the image classification problem, the construction of appropriate training data is important for improving the generalization ability of the classifier in particular when the size of the training data is small. We propose a method that quantitatively evaluates the typicality of a hematoxylin-and-eosin (H&E)-stained tissue slide from a set of immunohistochemical (IHC) stains and applies the typicality to instance selection for the construction of classifiers that predict the subtype of malignant lymphoma to improve the generalization ability.