  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.
