Making histopathology image classifiers robust to a wide range of real-world variability is a challenging task. Here, we describe a candidate deep learning solution for the Mitosis Domain Generalization Challenge 2022 (MIDOG) to address the problem of generalization for mitosis detection in images of hematoxylin-eosin-stained histology slides under high variability (scanner, tissue type and species variability). Our approach consists in training a rotation-invariant deep learning model using aggressive data augmentation with a training set enriched with hard negative examples and automatically selected negative examples from the unlabeled part of the challenge dataset. To optimize the performance of our models, we investigated a hard negative mining regime search procedure that lead us to train our best model using a subset of image patches representing 19.6% of our training partition of the challenge dataset. Our candidate model ensemble achieved a F1-score of .697 on the final test set after automated evaluation on the challenge platform, achieving the third best overall score in the MIDOG 2022 Challenge.
翻译:使组织病理学图象分类者对各种真实世界的变异性具有强大的活力是一项艰巨的任务。 在这里, 我们描述2022年Mitosidis Delain Generalization挑战( MIDOG)的候选深层次学习解决方案, 以解决在高变异性( 扫描器、 组织类型和物种变异性)下, 血氧素- 骨质素成像胶片中进行骨质疏松症检测的普通化问题。 我们的方法是使用积极的数据增强, 培训一组内容丰富, 并自动从挑战数据集的未标部分中选取负面例子。 为了优化模型的性能, 我们调查了一个硬性负面采矿制度搜索程序, 以利用代表我们挑战数据集培训分布的19.6%的相片段来培训我们的最佳模型。 我们的候选模型在挑战平台自动评估后, 在最后测试集中取得了F1- 697分, 在挑战平台上取得了MIDOG 2022挑战的第三个最佳总分数。