  Histopathological image classification is an important task in medical image
analysis. Recent approaches generally rely on weakly supervised learning due to
the ease of acquiring case-level labels from pathology reports. However,
patch-level classification is preferable in applications where only a limited
number of cases are available or when local prediction accuracy is critical. On
the other hand, acquiring extensive datasets with localized labels for training
is not feasible. In this paper, we propose a semi-supervised patch-level
histopathological image classification model, named CLASS-M, that does not
require extensively labeled datasets. CLASS-M is formed by two main parts: a
contrastive learning module that uses separated Hematoxylin and Eosin images
generated through an adaptive stain separation process, and a module with
pseudo-labels using MixUp. We compare our model with other state-of-the-art
models on two clear cell renal cell carcinoma datasets. We demonstrate that our
CLASS-M model has the best performance on both datasets. Our code is available
at github.com/BzhangURU/Paper_CLASS-M/tree/main
