Pathology Image Compression with Pre-trained Autoencoders
Journal:
arXiv
Published Date:
Mar 14, 2025
Abstract
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.
