Pathology Image Compression with Pre-trained Autoencoders
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913736331100160 |
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| author | Yellapragada, Srikar Graikos, Alexandros Triaridis, Kostas Li, Zilinghan Nandi, Tarak Nath Madduri, Ravi K Prasanna, Prateek Saltz, Joel Samaras, Dimitris |
| author_facet | Yellapragada, Srikar Graikos, Alexandros Triaridis, Kostas Li, Zilinghan Nandi, Tarak Nath Madduri, Ravi K Prasanna, Prateek Saltz, Joel Samaras, Dimitris |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11591 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pathology Image Compression with Pre-trained Autoencoders Yellapragada, Srikar Graikos, Alexandros Triaridis, Kostas Li, Zilinghan Nandi, Tarak Nath Madduri, Ravi K Prasanna, Prateek Saltz, Joel Samaras, Dimitris Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Pathology Image Compression with Pre-trained Autoencoders |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.11591 |