Whole slide image (WSI) processing is becoming part of the key components of
standard clinical diagnosis for various diseases. However, the direct
application of conventional image processing algorithms to WSI faces certain
obstacles because of WSIs' distinct property: the super-high resolution. The
performance of most WSI-related tasks relies on the efficacy of the backbone
which extracts WSI patch feature representations. Hence, we proposed BROW, a
foundation model for extracting better feature representations for WSIs, which
can be conveniently adapted to downstream tasks without or with slight
fine-tuning. The model takes transformer architecture, pretrained using
self-distillation framework. To improve model's robustness, techniques such as
patch shuffling have been employed. Additionally, the model leverages the
unique properties of WSIs, utilizing WSI's multi-scale pyramid to incorporate
an additional global view, thereby further enhancing its performance. We used
both private and public data to make up a large pretraining dataset, containing
more than 11000 slides, over 180M extracted patches, encompassing WSIs related
to various organs and tissues. To assess the effectiveness of \ourmodel, we run
a wide range of downstream tasks, including slide-level subtyping, patch-level
classification and nuclei instance segmentation. The results confirmed the
efficacy, robustness and good generalization ability of the proposed model.
This substantiates its potential as foundation model for WSI feature extraction
and highlights promising prospects for its application in WSI processing.

基于 BROW 模型，我们提出了一种用于提取 WSI 特征表示的基础模型，通过使用自蒸馏框架预训练的转换器结构，改进模型的鲁棒性，并利用 WSI 的多尺度金字塔来增强其性能，我们在各种器官和组织的 WSI 上进行实验，证实了该模型的有效性、鲁棒性和良好的泛化能力，从而突出了其在 WSI 处理中的潜力和应用前景。