Processing histopathological Whole Slide Images (WSI) leads to massive
storage requirements for clinics worldwide. Even after lossy image compression
during image acquisition, additional lossy compression is frequently possible
without substantially affecting the performance of deep learning-based (DL)
downstream tasks. In this paper, we show that the commonly used JPEG algorithm
is not best suited for further compression and we propose Stain Quantized
Latent Compression (SQLC ), a novel DL based histopathology data compression
approach. SQLC compresses staining and RGB channels before passing it through a
compression autoencoder (CAE ) in order to obtain quantized latent
representations for maximizing the compression. We show that our approach
yields superior performance in a classification downstream task, compared to
traditional approaches like JPEG, while image quality metrics like the
Multi-Scale Structural Similarity Index (MS-SSIM) is largely preserved. Our
method is online available.

我们提出了一种基于深度学习的组织病理学数据压缩方法，通过压缩染色和 RGB 通道，并结合压缩自编码器来获得量化的潜在表示，从而在分类任务中展现出卓越的性能，同时基本保留了图像质量。

基于染色卷积的 HE 染色组织病理图像学习式压缩

Learned Image Compression for HE-stained Histopathological Images via  Stain Deconvolution

Histopathological whole slide images (WSIs) classification has become a
foundation task in medical microscopic imaging processing. Prevailing
approaches involve learning WSIs as instance-bag representations, emphasizing
significant instances but struggling to capture the interactions between
instances. Additionally, conventional graph representation methods utilize
explicit spatial positions to construct topological structures but restrict the
flexible interaction capabilities between instances at arbitrary locations,
particularly when spatially distant. In response, we propose a novel dynamic
graph representation algorithm that conceptualizes WSIs as a form of the
knowledge graph structure. Specifically, we dynamically construct neighbors and
directed edge embeddings based on the head and tail relationships between
instances. Then, we devise a knowledge-aware attention mechanism that can
update the head node features by learning the joint attention score of each
neighbor and edge. Finally, we obtain a graph-level embedding through the
global pooling process of the updated head, serving as an implicit
representation for the WSI classification. Our end-to-end graph representation
learning approach has outperformed the state-of-the-art WSI analysis methods on
three TCGA benchmark datasets and in-house test sets. Our code is available at
this https URL

我们提出了一种新颖的动态图表示算法，将组织病理学切片图像（WSIs）概念化为知识图结构的形式，通过动态构建邻居和带向边嵌入的头尾实例之间的关系，利用知识感知的注意机制更新头结点特征，然后通过全局池化过程获得图级嵌入，作为 WSI 分类的隐式表示。我们的端到端图表示学习方法在三个 TCGA 基准数据集和内部测试集上优于最先进的 WSI 分析方法。