合成域目标增强（S-DOTA）提升数字病理学中的模型泛化能力
Synthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathology
AI总结：
研究提出S-DOTA方法（包括ST和SVA），通过CycleGAN和染色向量增强提升数字病理学模型在多域、多任务下的泛化能力，显著优于ICC校准和基线方法。
摘要：
机器学习算法有潜力改善数字病理学中的患者预后。然而，这些工具的泛化能力目前受限于对组织制备、染色程序和扫描设备变化的敏感性，这些变化会导致数字化切片中的域偏移。为了克服这一限制并提高模型泛化能力，我们研究了两种合成域目标增强（S-DOTA）方法的有效性，即基于CycleGAN的扫描仪转换（ST）和目标染色向量增强（SVA），并将它们与国际色彩联盟（ICC）基于配置文件的颜色校准（ICC Cal）方法以及使用传统亮度、颜色和噪声增强的基线方法进行了比较。我们评估了这些技术在各种任务和设置下提升模型泛化能力的能力：四个模型、两种模型类型（组织分割和细胞分类）、两种损失函数、六个实验室、六台扫描仪和三种适应症（肝细胞癌（HCC）、非酒精性脂肪性肝炎（NASH）、前列腺腺癌）。我们基于多个域上分布内（ID）和分布外（OOD）测试集的宏平均F1分数比较了这些方法，发现S-DOTA方法（即ST和SVA）在OOD数据上比ICC Cal和基线有显著改善，同时在ID数据上保持可比的性能。因此，我们证明S-DOTA可能有助于解决实际应用中由于域偏移导致的泛化问题。
英文摘要：
Machine learning algorithms have the potential to improve patient outcomes in digital pathology. However, generalization of these tools is currently limited by sensitivity to variations in tissue preparation, staining procedures and scanning equipment that lead to domain shift in digitized slides. To overcome this limitation and improve model generalization, we studied the effectiveness of two Synthetic DOmain-Targeted Augmentation (S-DOTA) methods, namely CycleGAN-enabled Scanner Transform (ST) and targeted Stain Vector Augmentation (SVA), and compared them against the International Color Consortium (ICC) profile-based color calibration (ICC Cal) method and a baseline method using traditional brightness, color and noise augmentations. We evaluated the ability of these techniques to improve model generalization to various tasks and settings: four models, two model types (tissue segmentation and cell classification), two loss functions, six labs, six scanners, and three indications (hepatocellular carcinoma (HCC), nonalcoholic steatohepatitis (NASH), prostate adenocarcinoma). We compared these methods based on the macro-averaged F1 scores on in-distribution (ID) and out-of-distribution (OOD) test sets across multiple domains, and found that S-DOTA methods (i.e., ST and SVA) led to significant improvements over ICC Cal and baseline on OOD data while maintaining comparable performance on ID data. Thus, we demonstrate that S-DOTA may help address generalization due to domain shift in real world applications.
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。
