Classification of gigapixel Whole Slide Images (WSIs) is an important
prediction task in the emerging area of computational pathology. There has been
a surge of research in deep learning models for WSI classification with
clinical applications such as cancer detection or prediction of molecular
mutations from WSIs. Most methods require expensive and labor-intensive manual
annotations by expert pathologists. Weakly supervised Multiple Instance
Learning (MIL) methods have recently demonstrated excellent performance;
however, they still require large slide-level labeled training datasets that
need a careful inspection of each slide by an expert pathologist. In this work,
we propose a fully unsupervised WSI classification algorithm based on mutual
transformer learning. Instances from gigapixel WSI (i.e., image patches) are
transformed into a latent space and then inverse-transformed to the original
space. Using the transformation loss, pseudo-labels are generated and cleaned
using a transformer label-cleaner. The proposed transformer-based pseudo-label
generation and cleaning modules mutually train each other iteratively in an
unsupervised manner. A discriminative learning mechanism is introduced to
improve normal versus cancerous instance labeling. In addition to unsupervised
classification, we demonstrate the effectiveness of the proposed framework for
weak supervision for cancer subtype classification as downstream analysis.
Extensive experiments on four publicly available datasets show excellent
performance compared to the state-of-the-art methods. We intend to make the
source code of our algorithm publicly available soon.

提出一种基于相互 Transformer 学习的全无监督 WSI 分类算法，并在公开数据集上进行了广泛实验，证明了该方法在 WSI 分类和癌症亚型分类方面的优越性能。