Representation learning from Gigapixel Whole Slide Images (WSI) poses a
significant challenge in computational pathology due to the complicated nature
of tissue structures and the scarcity of labeled data. Multi-instance learning
methods have addressed this challenge, leveraging image patches to classify
slides utilizing pretrained models using Self-Supervised Learning (SSL)
approaches. The performance of both SSL and MIL methods relies on the
architecture of the feature encoder. This paper proposes leveraging the Vision
Mamba (Vim) architecture, inspired by state space models, within the DINO
framework for representation learning in computational pathology. We evaluate
the performance of Vim against Vision Transformers (ViT) on the Camelyon16
dataset for both patch-level and slide-level classification. Our findings
highlight Vim's enhanced performance compared to ViT, particularly at smaller
scales, where Vim achieves an 8.21 increase in ROC AUC for models of similar
size. An explainability analysis further highlights Vim's capabilities, which
reveals that Vim uniquely emulates the pathologist workflow-unlike ViT. This
alignment with human expert analysis highlights Vim's potential in practical
diagnostic settings and contributes significantly to developing effective
representation-learning algorithms in computational pathology. We release the
codes and pretrained weights at
https://github.com/AtlasAnalyticsLab/Vim4Path.

基于 Vision Mamba 架构的图像表示学习在计算病理学中显示出显著的性能优势，并具有相对于 Vision Transformers 更好的实用性和模拟病理学家工作流程的能力。

Vim4Path: 自监督视觉玛巴用于组织病理学图像

Vim4Path: Self-Supervised Vision Mamba for Histopathology Images

In computation pathology, the pyramid structure of gigapixel Whole Slide
Images (WSIs) has recently been studied for capturing various information from
individual cell interactions to tissue microenvironments. This hierarchical
structure is believed to be beneficial for cancer diagnosis and prognosis
tasks. However, most previous hierarchical WSI analysis works (1) only
characterize local or global correlations within the WSI pyramids and (2) use
only unidirectional interaction between different resolutions, leading to an
incomplete picture of WSI pyramids. To this end, this paper presents a novel
Hierarchical Interaction Graph-Transformer (i.e., HIGT) for WSI analysis. With
Graph Neural Network and Transformer as the building commons, HIGT can learn
both short-range local information and long-range global representation of the
WSI pyramids. Considering that the information from different resolutions is
complementary and can benefit each other during the learning process, we
further design a novel Bidirectional Interaction block to establish
communication between different levels within the WSI pyramids. Finally, we
aggregate both coarse-grained and fine-grained features learned from different
levels together for slide-level prediction. We evaluate our methods on two
public WSI datasets from TCGA projects, i.e., kidney carcinoma (KICA) and
esophageal carcinoma (ESCA). Experimental results show that our HIGT
outperforms both hierarchical and non-hierarchical state-of-the-art methods on
both tumor subtyping and staging tasks.

本文提出了一种用于 WSI 分析的新型 Hierarchical Interaction Graph-Transformer（HIGT），通过学习 WSI 金字塔的局部和全局特征，并建立不同层次之间的双向交互，实现了肿瘤亚型和分期任务上的优于现有方法的性能。

HIGT：用于全幻灯片图像分析的分层交互图变换器

HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image  Analysis

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。