用预训练自编码器压缩病理图像，保细节还省空间。
Pathology Image Compression with Pre-trained Autoencoders
- 复用扩散模型用的自编码器做图像压缩。
- 压缩后图像在分割、分类任务上损失极小。
- 适合需要高效存储病理图像的研究者。
数字病理学中高分辨率全切片图像数量激增，带来存储、传输和计算效率挑战。传统压缩方法如JPEG虽减小文件大小，但常丢失对下游任务至关重要的细粒度表型信息。本文将为潜在扩散模型设计的自编码器（AE）改造为病理图像的高效学习压缩框架，系统评估了三种不同压缩率的AE模型，并使用病理基础模型衡量其重建能力。提出一种微调策略，优化针对病理学的感知度量指标以提升重建质量。在分割、切片分类和多实例学习等下游任务中验证，用AE压缩重建图像替代原始图像仅导致极小性能下降。此外，提出基于K均值聚类的潜变量量化方法，在保持重建质量的同时提升存储效率。相关权重已公开于https://huggingface.co/collections/StonyBrook-CVLab/pathology-fine-tuned-aes-67d45f223a659ff2e3402dd0。
原文摘要 · Abstract (English)
The growing volume of high-resolution Whole Slide Images in digital histopathology poses significant storage, transmission, and computational efficiency challenges. Standard compression methods, such as JPEG, reduce file sizes but often fail to preserve fine-grained phenotypic details critical for downstream tasks. In this work, we repurpose autoencoders (AEs) designed for Latent Diffusion Models as an efficient learned compression framework for pathology images. We systematically benchmark three AE models with varying compression levels and evaluate their reconstruction ability using pathology foundation models. We introduce a fine-tuning strategy to further enhance reconstruction fidelity that optimizes a pathology-specific learned perceptual metric. We validate our approach on downstream tasks, including segmentation, patch classification, and multiple instance learning, showing that replacing images with AE-compressed reconstructions leads to minimal performance degradation. Additionally, we propose a K-means clustering-based quantization method for AE latents, improving storage efficiency while maintaining reconstruction quality. We provide the weights of the fine-tuned autoencoders at https://huggingface.co/collections/StonyBrook-CVLab/pathology-fine-tuned-aes-67d45f223a659ff2e3402dd0.
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