通过深度学习增强病理全切片图像压缩中的诊断保真度
Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning
AI总结：
针对病理WSI压缩中JPEG损害诊断保真度的问题，提出通过约束深度特征相似性的深度学习压缩方法，并在PSNR、MS-SSIM和LPIPS上优于多种传统及DL方法。
摘要：
疾病的准确诊断通常依赖于在显微分辨率下对全切片图像（WSI）进行详尽检查。高效处理这些数据密集型图像需要有损压缩技术。本文研究了广泛使用的JPEG算法（当前临床标准）的局限性，并揭示了影响诊断保真度的严重图像伪影。为克服这些挑战，我们提出了一种专为病理图像定制的、基于深度学习（DL）的新型压缩方法。通过强制原始图像与压缩图像之间深度特征的特征相似性，与JPEG-XL、Webp及其他DL压缩方法相比，我们的方法取得了更优的峰值信噪比（PSNR）、多尺度结构相似性指数（MS-SSIM）和学习感知图像块相似度（LPIPS）得分。
英文摘要：
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.
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。
