  We propose a new method for cancer subtype classification from
histopathological images, which can automatically detect tumor-specific
features in a given whole slide image (WSI). The cancer subtype should be
classified by referring to a WSI, i.e., a large-sized image (typically
40,000x40,000 pixels) of an entire pathological tissue slide, which consists of
cancer and non-cancer portions. One difficulty arises from the high cost
associated with annotating tumor regions in WSIs. Furthermore, both global and
local image features must be extracted from the WSI by changing the
magnifications of the image. In addition, the image features should be stably
detected against the differences of staining conditions among the
hospitals/specimens. In this paper, we develop a new CNN-based cancer subtype
classification method by effectively combining multiple-instance, domain
adversarial, and multi-scale learning frameworks in order to overcome these
practical difficulties. When the proposed method was applied to malignant
lymphoma subtype classifications of 196 cases collected from multiple
hospitals, the classification performance was significantly better than the
standard CNN or other conventional methods, and the accuracy compared favorably
with that of standard pathologists.
