Abstract
Foundation models have revolutionized computational pathology by achieving remarkable success in high-level diagnostic tasks, yet the critical challenge of low-level image enhancement remains largely unaddressed. Real-world pathology images frequently suffer from degradations such as noise, blur, and low resolution due to slide preparation artifacts, staining variability, and imaging constraints, while the reliance on physical staining introduces significant costs, delays, and inconsistency. Although existing methods target individual problems like denoising or super-resolution, their task-specific designs lack the versatility to handle the diverse low-level vision challenges encountered in practice. To bridge this gap, we propose the first unified Low-level Pathology Foundation Model (LPFM), capable of enhancing image quality in restoration tasks, including super-resolution, deblurring, and denoising, as well as facilitating image translation tasks like virtual staining (H&E and special stains), all through a single adaptable architecture. Our approach introduces a contrastive pre-trained encoder that learns transferable, stain-invariant feature representations from 190 million unlabeled pathology images, enabling robust identification of degradation patterns. A unified conditional diffusion process dynamically adapts to specific tasks via textual prompts, ensuring precise control over output quality. Trained on a curated dataset of 87,810 whole slied images (WSIs) across 34 tissue types and 5 staining protocols, LPFM demonstrates statistically significant improvements (p<0.01) over state-of-the-art methods in most tasks (56/66), achieving Peak Signal-to-Noise Ratio (PSNR) gains of 10-15% for image restoration and Structural Similarity Index Measure (SSIM) improvements of 12-18% for virtual staining.
Abstract (translated)
基础模型在计算病理学中取得了重大突破，通过在高级诊断任务中的卓越成功实现了这一目标，然而低级图像增强的关键挑战仍未得到充分解决。实际的病理图像常常因载玻片制备缺陷、染色变异和成像限制而遭受噪声、模糊和分辨率降低等退化问题，依赖于物理染色又引入了显著的成本、延迟以及不一致性。尽管现有的方法针对诸如去噪或超分辨率这样的个别问题，但它们的任务特定设计缺乏处理实践中遇到的各种低级视觉挑战的灵活性。为弥合这一差距，我们提出了首个统一的低级病理基础模型（LPFM），该模型能够在包括超分辨率、去模糊和去噪在内的图像恢复任务中提升图像质量，并通过单个可适应架构促进诸如虚拟染色（H&E及特殊染料）等图像转换任务。我们的方法引入了一个对比预训练编码器，它从1.9亿张未标记的病理图像中学习到可以转移且不受染色影响的功能表示，从而能够稳健地识别退化模式。一个统一的条件扩散过程通过文本提示动态适应特定任务，确保精确控制输出质量。LPFM在87,810张全切片图像（WSIs）组成的精心策划的数据集上进行训练，这些图像是34种组织类型和5种染色协议的一部分，并且在大多数任务中（共66项中的56项），表现出统计显著性改进（p<0.01），实现了图像恢复的峰值信噪比（PSNR）提高10-15%，以及虚拟染色的结构相似度指数测量（SSIM）改善了12-18%。
URL
https://arxiv.org/abs/2509.01071
