Multiple-instance learning (MIL) is an attractive approach for digital
pathology applications as it reduces the costs related to data collection and
labelling. However, it is not clear how sensitive MIL is to clinically
realistic domain shifts, i.e., differences in data distribution that could
negatively affect performance, and if already existing metrics for detecting
domain shifts work well with these algorithms. We trained an attention-based
MIL algorithm to classify whether a whole-slide image of a lymph node contains
breast tumour metastases. The algorithm was evaluated on data from a hospital
in a different country and various subsets of this data that correspond to
different levels of domain shift. Our contributions include showing that MIL
for digital pathology is affected by clinically realistic differences in data,
evaluating which features from a MIL model are most suitable for detecting
changes in performance, and proposing an unsupervised metric named Fr\'echet
Domain Distance (FDD) for quantification of domain shifts. Shift measure
performance was evaluated through the mean Pearson correlation to change in
classification performance, where FDD achieved 0.70 on 10-fold cross-validation
models. The baselines included Deep ensemble, Difference of Confidence, and
Representation shift which resulted in 0.45, -0.29, and 0.56 mean Pearson
correlation, respectively. FDD could be a valuable tool for care providers and
vendors who need to verify if a MIL system is likely to perform reliably when
implemented at a new site, without requiring any additional annotations from
pathologists.

多实例学习在数字病理学应用中具有吸引力，但对于临床实际中的领域漂移敏感性问题尚不清楚，本研究通过训练一种基于注意力机制的多实例学习算法，在来自不同国家的医院数据以及对应于不同程度领域漂移的各个子集上进行分类乳腺肿瘤转移的全切片图像，并提出一种名为 Fréchet Domain Distance（FDD）的无监督度量方法来量化领域漂移。FDD 的性能表现通过与分类性能变化的平均皮尔逊相关系数衡量，10 折交叉验证模型下 FDD 的相关系数达到 0.70，作为评估基准的深度集成（Deep ensemble）、置信度差异（Difference of Confidence）和表达式漂移（Representation shift）方法分别获得的平均皮尔逊相关系数为 0.45、-0.29 和 0.56。FDD 可以成为医疗服务提供者和供应商的有价值工具，无需额外的病理学注释就能验证多实例学习系统在新的实施点上是否能够可靠运行。