Abstract
Multiple Instance learning (MIL) models have been extensively used in pathology to predict biomarkers and risk-stratify patients from gigapixel-sized images. Machine learning problems in medical imaging often deal with rare diseases, making it important for these models to work in a label-imbalanced setting. Furthermore, these imbalances can occur in out-of-distribution (OOD) datasets when the models are deployed in the real-world. We leverage the idea that decoupling feature and classifier learning can lead to improved decision boundaries for label imbalanced datasets. To this end, we investigate the integration of supervised contrastive learning with multiple instance learning (SC-MIL). Specifically, we propose a joint-training MIL framework in the presence of label imbalance that progressively transitions from learning bag-level representations to optimal classifier learning. We perform experiments with different imbalance settings for two well-studied problems in cancer pathology: subtyping of non-small cell lung cancer and subtyping of renal cell carcinoma. SC-MIL provides large and consistent improvements over other techniques on both in-distribution (ID) and OOD held-out sets across multiple imbalanced settings.
Abstract (translated)
多实例学习(MIL)模型在病理学中被广泛使用，从Gigapixel大小的图像中预测生物标记和风险分类患者。医学影像学中的机器学习问题通常涉及罕见的疾病，因此这些模型必须在标签不平衡的环境下工作。此外，当模型在现实世界部署时，标签不平衡可能会发生在非均匀分布的数据集上。我们利用的是 feature 和分类器学习解耦的概念，这可以导致标签不平衡的数据集决策边界改善。为此，我们研究了监督对比学习与多实例学习(SC-MIL)的集成。具体而言，我们提出了在标签不平衡的情况下进行联合训练的 MIL 框架，并逐步从学习袋级表示转移到最佳分类器学习。我们对两个在癌症病理学中被广泛研究的问题的不同类型进行了实验：非小细胞肺癌和肺癌的不同类型。SC-MIL 在均匀分布(ID)和 OOD 保留组中提供了比其他技术大且一致的改善。
URL
https://arxiv.org/abs/2303.13405
