用深度学习提升病理切片图像压缩质量，更保真。
Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning
- 基于深度特征相似性约束，优化压缩算法。
- 在PSNR、SSIM和感知相似性上优于JPEG-XL等方法。
- 适合需要高保真图像的病理诊断场景。
疾病准确诊断常依赖于以显微分辨率全面检查全切片图像（WSI）。高效处理这些数据密集型图像需采用有损压缩技术。本文研究了广泛使用的JPEG算法（当前临床标准）的局限性，发现其存在严重图像伪影，影响诊断保真度。为克服此问题，我们提出一种专为病理图像设计的新型深度学习压缩方法。通过强制原图与压缩图的深层特征保持相似性，该方法在峰值信噪比（PSNR）、多尺度结构相似性指数（MS-SSIM）和学习感知图像块相似性（LPIPS）指标上，均优于JPEG-XL、WebP及其他深度学习压缩方法。
原文摘要 · Abstract (English)
Accurate diagnosis of disease often depends on the exhaustive examination of Whole Slide Images (WSI) at microscopic resolution. Efficient handling of these data-intensive images requires lossy compression techniques. This paper investigates the limitations of the widely-used JPEG algorithm, the current clinical standard, and reveals severe image artifacts impacting diagnostic fidelity. To overcome these challenges, we introduce a novel deep-learning (DL)-based compression method tailored for pathology images. By enforcing feature similarity of deep features between the original and compressed images, our approach achieves superior Peak Signal-to-Noise Ratio (PSNR), Multi-Scale Structural Similarity Index (MS-SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) scores compared to JPEG-XL, Webp, and other DL compression methods.
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