DeepAf：用于数字病理学的单次空间光谱自动对焦模型
DeepAf: One-Shot Spatiospectral Auto-Focus Model for Digital Pathology
AI总结：
DeepAf提出一种结合空间与光谱特征的混合架构，实现单次自动对焦，将传统显微镜转为高效扫描仪，对焦时间减少80%，并保持诊断精度。
摘要：
虽然全玻片成像（WSI）扫描仪仍然是病理样本数字化的金标准，但其高昂的成本限制了在许多医疗环境中的可及性。其他低成本解决方案也面临关键限制：自动显微镜难以在不同组织形态下保持一致的聚焦，传统自动对焦方法需要耗时的焦栈，而现有的深度学习方法要么需要多张输入图像，要么缺乏跨组织类型和染色方案泛化的能力。我们引入了一种由DeepAf驱动的新型自动化显微镜系统，DeepAf是一种新颖的自动对焦框架，通过混合架构独特地结合空间和光谱特征，用于单次对焦预测。所提出的网络利用提取的空间光谱特征自动回归到最佳焦点的距离，并调整控制参数以获得最佳图像结果。我们的系统将传统显微镜转变为高效的玻片扫描仪，与基于堆栈的方法相比，对焦时间减少了80%，同时在同实验室样本上实现了0.18微米的聚焦精度，以一半的输入需求匹配了双图像方法（0.19微米）的性能。DeepAf展示了稳健的跨实验室泛化能力，仅有0.72%的错误聚焦预测，且90%的预测在景深范围内。通过对536个脑组织样本的广泛临床研究，我们的系统在4倍放大倍率下实现了癌症分类0.90的AUC，这是在比典型20倍WSI扫描更低的放大倍率下取得的显著成就。这产生了一种全面的软硬件设计，使得在资源受限的环境中实现可访问、实时的数字病理学成为可能，同时保持诊断准确性。
英文摘要：
While Whole Slide Imaging (WSI) scanners remain the gold standard for digitizing pathology samples, their high cost limits accessibility in many healthcare settings. Other low-cost solutions also face critical limitations: automated microscopes struggle with consistent focus across varying tissue morphology, traditional auto-focus methods require time-consuming focal stacks, and existing deep-learning approaches either need multiple input images or lack generalization capability across tissue types and staining protocols. We introduce a novel automated microscopic system powered by DeepAf, a novel auto-focus framework that uniquely combines spatial and spectral features through a hybrid architecture for single-shot focus prediction. The proposed network automatically regresses the distance to the optimal focal point using the extracted spatiospectral features and adjusts the control parameters for optimal image outcomes. Our system transforms conventional microscopes into efficient slide scanners, reducing focusing time by 80% compared to stack-based methods while achieving focus accuracy of 0.18 μm on the same-lab samples, matching the performance of dual-image methods (0.19 μm) with half the input requirements. DeepAf demonstrates robust cross-lab generalization with only 0.72% false focus predictions and 90% of predictions within the depth of field. Through an extensive clinical study of 536 brain tissue samples, our system achieves 0.90 AUC in cancer classification at 4x magnification, a significant achievement at lower magnification than typical 20x WSI scans. This results in a comprehensive hardware-software design enabling accessible, real-time digital pathology in resource-constrained settings while maintaining diagnostic accuracy.
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