Intra-Video Positive Pairs in Self-Supervised Learning for Ultrasound

Abstract

The videographic nature of ultrasound offers flexibility for defining the similarity relationship between pairs of images for self-supervised learning (SSL). In this study, we investigated the effect of utilizing proximal, distinct images from the same ultrasound video as pairs for joint embedding SSL. Additionally, we introduced a sample weighting scheme that increases the weight of closer image pairs and demonstrated how it can be integrated into SSL objectives. Named Intra-Video Positive Pairs (IVPP), the method surpassed previous ultrasound-specific contrastive learning methods’ average test accuracy on COVID-19 classification with the POCUS dataset by ≥ 1.3%. Investigations revealed that some combinations of IVPP hyperparameters can lead to improved or worsened performance, depending on the downstream task