论文标题
融合：通过融合归一化统计数据完全无监督的测试时间染色适应
FUSION: Fully Unsupervised Test-Time Stain Adaptation via Fused Normalization Statistics
论文作者
论文摘要
染色揭示了抽吸物的微结构，同时创建组织病理学幻灯片。染色变异被定义为源和目标之间的色差差异，是由染色过程中的特征变化引起的，导致分布变化和目标的性能差。染色归一化的目的是将目标的色谱分布与源的色度分布相匹配。然而，染色归一化会导致潜在的形态变形，从而导致错误的诊断。我们提出了Fusion，这是一种通过在无监督的测试时间方案中调整模型到目标来促进污渍适应的新方法，从而消除了目标末端进行大量标记的必要性。 Fusion通过更改目标的批准统计数据并使用加权因子将其与源统计融合来起作用。根据加权因子，该算法减少到两个极端之一。尽管缺乏培训或监督，但融合超过了针对分类和密集预测（分段）的现有等效算法，如两个公共数据集上的全面实验所证明的那样。
Staining reveals the micro structure of the aspirate while creating histopathology slides. Stain variation, defined as a chromatic difference between the source and the target, is caused by varying characteristics during staining, resulting in a distribution shift and poor performance on the target. The goal of stain normalization is to match the target's chromatic distribution to that of the source. However, stain normalisation causes the underlying morphology to distort, resulting in an incorrect diagnosis. We propose FUSION, a new method for promoting stain-adaption by adjusting the model to the target in an unsupervised test-time scenario, eliminating the necessity for significant labelling at the target end. FUSION works by altering the target's batch normalization statistics and fusing them with source statistics using a weighting factor. The algorithm reduces to one of two extremes based on the weighting factor. Despite the lack of training or supervision, FUSION surpasses existing equivalent algorithms for classification and dense predictions (segmentation), as demonstrated by comprehensive experiments on two public datasets.
