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We propose a method that quantitatively evaluates the typicality of a hematoxylin-and-eosin (H&amp;E)-stained tissue slide from a set of immunohistochemical (IHC) stains and applies the <jats:italic>typicality<\/jats:italic> to instance selection for the construction of classifiers that predict the subtype of malignant lymphoma to improve the generalization ability.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We define the typicality of the H&amp;E-stained tissue slides by the ratio of the probability density of the IHC staining patterns on low-dimensional embedded space. Employing a multiple-instance-learning-based convolutional neural network for the construction of the subtype classifier without the annotations indicating cancerous regions in whole slide images, we select the training data by referring to the evaluated typicality to improve the generalization ability. We demonstrate the effectiveness of the instance selection based on the proposed typicality in a three-class subtype classification of 262 malignant lymphoma cases.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In the experiment, we confirmed that the subtypes of typical instances could be predicted more accurately than those of atypical instances. Furthermore, it was confirmed that instance selection for the training data based on the proposed typicality improved the generalization ability of the classifier, wherein the classification accuracy was improved from 0.664 to 0.683 compared with the baseline method when the training data was constructed focusing on typical instances.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The experimental results showed that the typicality of the H&amp;E-stained tissue slides computed from IHC staining patterns is useful as a criterion for instance selection to enhance the generalization ability, and this typicality could be employed for instance selection under some practical limitations.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-021-02549-0","type":"journal-article","created":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T15:02:46Z","timestamp":1644591766000},"page":"1379-1389","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Subtype classification of malignant lymphoma using immunohistochemical staining pattern"],"prefix":"10.1007","volume":"17","author":[{"given":"Noriaki","family":"Hashimoto","sequence":"first","affiliation":[]},{"given":"Kaho","family":"Ko","sequence":"additional","affiliation":[]},{"given":"Tatsuya","family":"Yokota","sequence":"additional","affiliation":[]},{"given":"Kei","family":"Kohno","sequence":"additional","affiliation":[]},{"given":"Masato","family":"Nakaguro","sequence":"additional","affiliation":[]},{"given":"Shigeo","family":"Nakamura","sequence":"additional","affiliation":[]},{"given":"Ichiro","family":"Takeuchi","sequence":"additional","affiliation":[]},{"given":"Hidekata","family":"Hontani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,11]]},"reference":[{"key":"2549_CR1","unstructured":"Swerdlow SH, for Research\u00a0on Cancer IA (2017) WHO classification of tumours of haematopoietic and lymphoid tissues, rev. 4th ed edn. 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