基于染色解卷积的HE染色组织病理学图像学习型图像压缩
Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution
AI总结：
针对组织病理学全切片图像的海量存储需求，提出基于染色解卷积的SQLC深度学习压缩方法，通过先压缩染色与RGB通道再经CAE生成量化潜表示提升压缩效率，在分类任务性能优于JPEG且图像质量基本保留。
摘要：
处理组织病理学全切片图像（WSI）给全球诊所带来了海量存储需求。即便在图像采集过程中进行了有损压缩，通常仍可在不显著影响基于深度学习（DL）的下游任务性能的前提下进行额外有损压缩。本文表明常用的JPEG算法并非进一步压缩的最优选择，提出了一种新型基于DL的组织病理学数据压缩方法——Stain Quantized Latent Compression（SQLC，染色量化潜空间压缩）。SQLC先对染色通道和RGB通道进行压缩，再输入压缩自编码器（CAE）以获得量化潜表示，实现压缩率最大化。实验表明，与JPEG等传统方法相比，该方法在分类下游任务中性能更优，同时多尺度结构相似性指数（MS-SSIM）等图像质量指标基本得以保留。该方法已在线公开。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
