Recently, pathological diagnosis, the gold standard for cancer diagnosis, has
achieved superior performance by combining the Transformer with the multiple
instance learning (MIL) framework using whole slide images (WSIs). However, the
giga-pixel nature of WSIs poses a great challenge for the quadratic-complexity
self-attention mechanism in Transformer to be applied in MIL. Existing studies
usually use linear attention to improve computing efficiency but inevitably
bring performance bottlenecks. To tackle this challenge, we propose a MamMIL
framework for WSI classification by cooperating the selective structured state
space model (i.e., Mamba) with MIL for the first time, enabling the modeling of
instance dependencies while maintaining linear complexity. Specifically, to
solve the problem that Mamba can only conduct unidirectional one-dimensional
(1D) sequence modeling, we innovatively introduce a bidirectional state space
model and a 2D context-aware block to enable MamMIL to learn the bidirectional
instance dependencies with 2D spatial relationships. Experiments on two
datasets show that MamMIL can achieve advanced classification performance with
smaller memory footprints than the state-of-the-art MIL frameworks based on the
Transformer. The code will be open-sourced if accepted.

通过将 Transformer 与多实例学习（MIL）框架结合使用整张切片图像（WSIs），我们提出了一种 MamMIL 框架，首次将选择性结构状态空间模型（即 Mamba）与 MIL 结合起来，实现了对实例依赖性的建模并保持了线性复杂性。在两个数据集上的实验证明，MamMIL 可以以比基于 Transformer 的最先进的 MIL 框架更小的内存占用来实现先进的分类性能。

MamMIL：基于状态空间模型的整片切片图像多实例学习

MamMIL: Multiple Instance Learning for Whole Slide Images with State  Space Models

Artificial intelligence (AI) solutions that automatically extract information
from digital histology images have shown great promise for improving
pathological diagnosis. Prior to routine use, it is important to evaluate their
predictive performance and obtain regulatory approval. This assessment requires
appropriate test datasets. However, compiling such datasets is challenging and
specific recommendations are missing.
A committee of various stakeholders, including commercial AI developers,
pathologists, and researchers, discussed key aspects and conducted extensive
literature reviews on test datasets in pathology. Here, we summarize the
results and derive general recommendations for the collection of test datasets.
We address several questions: Which and how many images are needed? How to
deal with low-prevalence subsets? How can potential bias be detected? How
should datasets be reported? What are the regulatory requirements in different
countries?
The recommendations are intended to help AI developers demonstrate the
utility of their products and to help regulatory agencies and end users verify
reported performance measures. Further research is needed to formulate criteria
for sufficiently representative test datasets so that AI solutions can operate
with less user intervention and better support diagnostic workflows in the
future.

人工智能在数字组织学图像中的自动信息提取已被证明可以改善病理诊断。然而，在正式使用前，需要评估其预测性能并获得监管机构的认可，而这需要恰当的测试数据集，本文总结了病理测试数据集的一般建议，旨在帮助人工智能开发者证明其产品的实用性，并帮助监管机构和最终用户验证所报导的性能指标。