发表机构
University of Piraeus（比雷埃夫斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究组织检测方法对扩散式病理伪影检测假阳性的影响，发现基于熵的检测可显著降低假阳性，且增益源于池组成而非大小。
AI 中文摘要
全切片图像的单类伪影检测器从干净的训练池中学习正常组织，并标记偏离该池的异常。该池由预处理流程构建，其中组织检测步骤通常被视为中性的。我们测试了该假设是否成立。在16张带注释的TCGA切片上，我们使用不同的组织检测方法重建了基于扩散的检测器的干净池，并通过四折交叉验证比较了所得模型。每张切片的饱和度-Otsu检测排除了正常组织，主要是具有大透明空间的组织，如脂肪组织和肺泡实质，并且在带有厚标记墨水的切片上保留了墨水而排除了普通组织。将其替换为基于熵的检测，在保留的干净切片上将假阳性分数从0.102降至0.016，在每一折和使用第二个训练种子的情况下均如此，且不损失灵敏度；增益来自池的组成，而非其大小。在三种组织检测方法中，假阳性随池中此类透明空间组织的比例而变化，该统计量无需标签或训练（0.103、0.016和0.009）。该效应未以相同规模转移到基于基础模型特征的最近邻检测器。在外部队列中，经过整理的池将干净对照假阳性降低了约20%，远低于TCGA内部，且剩余的跨中心损失无法用染色差异解释。对于单类质量控制，组织检测决定了模型学习为正常的内容，因此应相应选择和报告。
英文摘要
One-class artifact detectors for whole-slide images learn normal tissue from a clean training pool and flag departures from it. The pool is built by a preprocessing pipeline whose tissue-detection step is usually treated as neutral. We tested whether it is. On 16 annotated slides from The Cancer Genome Atlas, we rebuilt the clean pool of a diffusion-based detector with different tissue detection methods and compared the resulting models in a four-fold cross-validation. Per-slide saturation-Otsu detection excluded normal tissue, chiefly tissue with large clear spaces such as adipose tissue and alveolar parenchyma, and on slides with thick marker ink kept the ink while excluding ordinary tissue. Replacing it with entropy-based detection reduced the false-positive fraction on held-out clean slides from 0.102 to 0.016, in every fold and with a second training seed, without loss of sensitivity; the gain came from the composition of the pool, not its size. Across three tissue detection methods, false positives followed the fraction of such clear-space tissue in the pool, a statistic that needs no labels or training (0.103, 0.016 and 0.009). The effect did not carry over at the same size to a nearest-neighbour detector on foundation-model features. On an external cohort, the curated pool lowered clean-control false positives by about 20%, far less than on the development slides, and the remaining cross-center loss was not explained by stain differences. For one-class quality control, tissue detection decides what the model learns as normal and should be chosen and reported accordingly.
Comments29 pages, 3 figures, 4 tables, including supplementary material. Code, data and models: https://doi.org/10.5281/zenodo.23016733
