解锁数字病理学的潜力：压缩的新基线
Unlocking the Potential of Digital Pathology: Novel Baselines for Compression
AI总结：
本研究联合评估数字病理全切片图像的有损压缩方案，提出基于特征相似性的新指标，以统一评估压缩质量并加速临床采用。
摘要：
数字病理学为组织病理学图像分析中的临床实践转型提供了突破性机遇，但面临一个重大障碍：病理全切片图像（WSI）的庞大数据文件大小。虽然当前数字病理学解决方案依赖有损JPEG压缩来解决此问题，但有损压缩可能引入颜色和纹理差异，从而可能影响临床决策。尽管先前的研究分别独立地处理感知图像质量和下游任务性能，我们在四个不同数据集上联合评估了压缩方案在感知质量和下游任务质量方面的表现。此外，我们收集了一个初始未压缩的数据集，用于对压缩方案进行无偏的感知评估。我们的结果表明，针对感知质量进行微调的深度学习模型在进一步压缩WSI时优于JPEG-XL或WebP等传统压缩方案。然而，这些模型对训练数据中存在的压缩伪影表现出显著偏差，并且难以在不同压缩方案之间进行泛化。我们提出了一种基于原始文件与压缩文件之间特征相似性的新颖评估指标，该指标与压缩WSI上的实际下游性能高度吻合。我们的指标能够对有损压缩方案进行通用且标准化的评估，并减轻了独立评估不同下游任务的需求。我们的研究为评估WSI的有损压缩方案提供了新颖见解，并鼓励对有损压缩方案进行统一评估，以加速数字病理学的临床采用。
英文摘要：
Digital pathology offers a groundbreaking opportunity to transform clinical practice in histopathological image analysis, yet faces a significant hurdle: the substantial file sizes of pathological Whole Slide Images (WSI). While current digital pathology solutions rely on lossy JPEG compression to address this issue, lossy compression can introduce color and texture disparities, potentially impacting clinical decision-making. While prior research addresses perceptual image quality and downstream performance independently of each other, we jointly evaluate compression schemes for perceptual and downstream task quality on four different datasets. In addition, we collect an initially uncompressed dataset for an unbiased perceptual evaluation of compression schemes. Our results show that deep learning models fine-tuned for perceptual quality outperform conventional compression schemes like JPEG-XL or WebP for further compression of WSI. However, they exhibit a significant bias towards the compression artifacts present in the training data and struggle to generalize across various compression schemes. We introduce a novel evaluation metric based on feature similarity between original files and compressed files that aligns very well with the actual downstream performance on the compressed WSI. Our metric allows for a general and standardized evaluation of lossy compression schemes and mitigates the requirement to independently assess different downstream tasks. Our study provides novel insights for the assessment of lossy compression schemes for WSI and encourages a unified evaluation of lossy compression schemes to accelerate the clinical uptake of digital pathology.
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