Large medical imaging datasets can be cheaply and quickly annotated with low-confidence, weak labels (e.g., radiological scores). Access to high-confidence labels, such as histology-based diagnoses, is rare and costly. Pretraining strategies, like contrastive learning (CL) methods, can leverage unlabeled or weakly-annotated datasets. These methods typically require large batch sizes, which poses a difficulty in the case of large 3D images at full resolution, due to limited GPU memory. Nevertheless, volumetric positional information about the spatial context of each 2D slice can be very important for some medical applications. In this work, we propose an efficient weakly-supervised positional (WSP) contrastive learning strategy where we integrate both the spatial context of each 2D slice and a weak label via a generic kernel-based loss function. We illustrate our method on cirrhosis prediction using a large volume of weakly-labeled images, namely radiological low-confidence annotations, and small strongly-labeled (i.e., high-confidence) datasets. The proposed model improves the classification AUC by 5% with respect to a baseline model on our internal dataset, and by 26% on the public LIHC dataset from the Cancer Genome Atlas. The code is available at: https://github.com/Guerbet-AI/wsp-contrastive.

翻译：大型医学影像数据集可以通过低成本、快速的方式获得低置信度的弱标签（如放射学评分）。而获取高置信度标签（如基于组织学的诊断）则既罕见又昂贵。对比学习等预训练策略可以利用未标注或弱标注数据集。这些方法通常需要较大的批次大小，但在全分辨率大规模3D图像的情况下，由于GPU内存限制，这带来困难。然而，对于某些医学应用，关于每个2D切片空间上下文的三维位置信息非常重要。在这项工作中，我们提出了一种高效的弱监督位置对比学习策略，通过基于核的通用损失函数，将每个2D切片的空间上下文和弱标签整合在一起。我们利用大量弱标注图像（即低置信度的放射学标注）和小规模强标注（即高置信度）数据集，在肝硬化预测任务中展示了该方法的效果。与基线模型相比，所提模型在内部数据集上将分类AUC提升了5%，在癌症基因组图谱的公共LIHC数据集上提升了26%。代码已开源：https://github.com/Guerbet-AI/wsp-contrastive.