Knowledge-guided pretext learning for utero-placental interface detection

Abstract

Modern machine learning systems, such as convolutional neural networks rely on a rich collection of training data to learn discriminative representations. In many medical imaging applications, unfortunately, collecting a large set of well-annotated data is prohibitively expensive. To overcome data shortage and facilitate representation learning, we develop Knowledge-guided Pretext Learning (KPL) that learns anatomy-related image representations in a pretext task under the guidance of knowledge from the downstream target task. In the context of utero-placental interface detection in placental ultrasound, we find that KPL substantially improves the quality of the learned representations without consuming data from external sources such as ImageNet. It outperforms the widely adopted supervised pre-training and self-supervised learning approaches across model capacities and dataset scales. Our results suggest that pretext learning is a promising direction for representation learning in medical image analysis, especially in the small data regime.