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。

无监督互相转换器学习用于多千兆像素全切片图像分类

Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide  Image Classification

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.

本文提出了一种利用深度学习中的多例、领域对抗和多尺度学习框架，从组织病理学图像中识别癌症亚型的新方法，并在 196 个病例中测试，表明该方法比标准 CNN 或传统方法更准确，且与标准病理学家的准确性相当。