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通过低置信度的弱标签（如放射学评分）快速且低成本地进行标注。而高置信度标签（如基于组织病理学的诊断）的获取则罕见且昂贵。对比学习等预训练策略可利用未标注或弱标注数据集。这些方法通常需要大批次大小，但全分辨率大尺寸三维图像受限于GPU内存，难以满足该需求。然而，对于某些医学应用而言，二维切片空间背景的立体位置信息至关重要。本研究提出一种高效的弱监督位置对比学习策略，通过基于核函数的通用损失函数，整合二维切片的空间背景与弱标签信息。我们利用大量弱标注图像（即放射学低置信度标注）与小规模强标注（高置信度）数据集，在肝硬化预测任务中验证了该方法。相较于基线模型，该模型在内部数据集上分类AUC提升5%，在癌症基因组图谱公开LIHC数据集上提升26%。代码获取地址：https://github.com/Guerbet-AI/wsp-contrastive。