Histopathological whole slide image (WSI) analysis with deep learning has
become a research focus in computational pathology. The current paradigm is
mainly based on multiple instance learning (MIL), in which approaches with
Transformer as the backbone are well discussed. These methods convert WSI tasks
into sequence tasks by representing patches as tokens in the WSI sequence.
However, the feature complexity brought by high heterogeneity and the
ultra-long sequences brought by gigapixel size makes Transformer-based MIL
suffer from the challenges of high memory consumption, slow inference speed,
and lack of performance. To this end, we propose a retentive MIL method called
RetMIL, which processes WSI sequences through hierarchical feature propagation
structure. At the local level, the WSI sequence is divided into multiple
subsequences. Tokens of each subsequence are updated through a parallel linear
retention mechanism and aggregated utilizing an attention layer. At the global
level, subsequences are fused into a global sequence, then updated through a
serial retention mechanism, and finally the slide-level representation is
obtained through a global attention pooling. We conduct experiments on two
public CAMELYON and BRACS datasets and an public-internal LUNG dataset,
confirming that RetMIL not only achieves state-of-the-art performance but also
significantly reduces computational overhead. Our code will be accessed
shortly.

通过提出的基于保留机制的 RetMIL 方法，实现了对组织病理学全幻灯片图像的分析，同时在性能和计算开销方面取得了显著的提升。

RetMIL：组织病理学全切片图像分类的记忆多实例学习

RetMIL: Retentive Multiple Instance Learning for Histopathological Whole  Slide Image Classification

Poor performance of quantitative analysis in histopathological Whole Slide
Images (WSI) has been a significant obstacle in clinical practice. Annotating
large-scale WSIs manually is a demanding and time-consuming task, unlikely to
yield the expected results when used for fully supervised learning systems.
Rarely observed disease patterns and large differences in object scales are
difficult to model through conventional patient intake. Prior methods either
fall back to direct disease classification, which only requires learning a few
factors per image, or report on average image segmentation performance, which
is highly biased towards majority observations. Geometric image augmentation is
commonly used to improve robustness for average case predictions and to enrich
limited datasets. So far no method provided sampling of a realistic posterior
distribution to improve stability, e.g. for the segmentation of imbalanced
objects within images. Therefore, we propose a new approach, based on diffusion
models, which can enrich an imbalanced dataset with plausible examples from
underrepresented groups by conditioning on segmentation maps. Our method can
simply expand limited clinical datasets making them suitable to train machine
learning pipelines, and provides an interpretable and human-controllable way of
generating histopathology images that are indistinguishable from real ones to
human experts. We validate our findings on two datasets, one from the public
domain and one from a Kidney Transplant study.

通过扩充临床数据集中的少数样本，提高机器学习模型稳定性的一种新方法 —— 基于扩散模型的图像生成方法，可以生成与真实图像难以区分的组织病理学图像。

组织病理学中基于现实数据丰富的强健图像分割

Realistic Data Enrichment for Robust Image Segmentation in  Histopathology

Automated classification of histopathological whole-slide images (WSI) of
breast tissue requires analysis at very high resolutions with a large
contextual area. In this paper, we present context-aware stacked convolutional
neural networks (CNN) for classification of breast WSIs into normal/benign,
ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We first
train a CNN using high pixel resolution patches to capture cellular level
information. The feature responses generated by this model are then fed as
input to a second CNN, stacked on top of the first. Training of this stacked
architecture with large input patches enables learning of fine-grained
(cellular) details and global interdependence of tissue structures. Our system
is trained and evaluated on a dataset containing 221 WSIs of H&E stained breast
tissue specimens. The system achieves an AUC of 0.962 for the binary
classification of non-malignant and malignant slides and obtains a three class
accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC,
demonstrating its potentials for routine diagnostics.

本文提出一种上下文感知的堆叠卷积神经网络模型，用于对乳腺癌组织 WSI 的细胞级信息进行分类，成功实现对病理组织的诊断和分类。