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We propose a new method for cancer subtype classification fromhistopathological images, which can automatically detect tumor-specificfeatures in a given whole slide image (WSI). The cancer subtype should beclassified by referring to a WSI, i.e., a large-sized image (typically40,000x40,000 pixels) of an entire pathological tissue slide, which consists ofcancer and non-cancer portions. One difficulty arises from the high costassociated with annotating tumor regions in WSIs. Furthermore, both global andlocal image features must be extracted from the WSI by changing themagnifications of the image. In addition, the image features should be stablydetected against the differences of staining conditions among thehospitals/specimens. In this paper, we develop a new CNN-based cancer subtypeclassification method by effectively combining multiple-instance, domainadversarial, and multi-scale learning frameworks in order to overcome thesepractical difficulties. When the proposed method was applied to malignantlymphoma subtype classifications of 196 cases collected from multiplehospitals, the classification performance was significantly better than thestandard CNN or other conventional methods, and the accuracy compared favorablywith that of standard pathologists.

Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images