EXAONEPath 1.0：用于病理学的Patch级基础模型
EXAONEPath 1.0 Patch-level Foundation Model for Pathology
AI总结：
针对病理WSI自监督模型的特征坍缩问题，提出在染色归一化patch上训练的基础模型EXAONEPath，有效提升泛化能力并在六项下游任务中以更少参数实现更优性能。
摘要：
数字病理学的最新进展推动了众多基础模型的开发，这些模型利用自监督学习对从千兆像素全切片图像（WSIs）中提取的patches进行处理。虽然这种方法利用了大量未标记数据，但我们发现了一个重大问题：从这些自监督模型提取的特征倾向于按单个WSI聚类，我们将这一现象称为WSI特定特征坍缩。这个问题可能会限制模型的泛化能力及其在各种下游任务上的性能。为了解决这个问题，我们引入了EXAONEPath，这是一种在经过染色归一化的patches上训练的新型基础模型。染色归一化有助于减少由不同实验室和扫描仪引起的颜色变异性，使模型能够学习更一致的特征。EXAONEPath使用从总计34,795张WSIs中提取的285,153,903个patches进行训练。我们的实验表明，EXAONEPath显著缓解了特征坍缩问题，这表明模型学习到了更具泛化性的特征，而不是过拟合于单个WSI的特征。我们在六个下游任务数据集上将EXAONEPath与最先进的模型进行了比较，结果表明，相对于使用的WSI数量和模型参数量，EXAONEPath取得了更优的性能。这表明染色归一化的应用大幅提升了模型的效率和泛化能力。
英文摘要：
Recent advancements in digital pathology have led to the development of numerous foundational models that utilize self-supervised learning on patches extracted from gigapixel whole slide images (WSIs). While this approach leverages vast amounts of unlabeled data, we have discovered a significant issue: features extracted from these self-supervised models tend to cluster by individual WSIs, a phenomenon we term WSI-specific feature collapse. This problem can potentially limit the model's generalization ability and performance on various downstream tasks. To address this issue, we introduce EXAONEPath, a novel foundational model trained on patches that have undergone stain normalization. Stain normalization helps reduce color variability arising from different laboratories and scanners, enabling the model to learn more consistent features. EXAONEPath is trained using 285,153,903 patches extracted from a total of 34,795 WSIs. Our experiments demonstrate that EXAONEPath significantly mitigates the feature collapse problem, indicating that the model has learned more generalized features rather than overfitting to individual WSI characteristics. We compared EXAONEPath with state-of-the-art models across six downstream task datasets, and our results show that EXAONEPath achieves superior performance relative to the number of WSIs used and the model's parameter count. This suggests that the application of stain normalization has substantially improved the model's efficiency and generalization capabilities.
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。
