# Contrastive Self-Supervised Learning for Spatio-Temporal Analysis of Lung Ultrasound Videos

arXiv: https://arxiv.org/abs/2310.10689 · HTML: https://mlsanity.com/paper/2310.10689/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2023.

## Built on by (most cited first, 4 of 4)

- [Self-Supervised Ultrasound-Video Segmentation with Feature Prediction and 3D Localised Loss](https://mlsanity.com/paper/2507.18424/) (2025)
- [The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound](https://mlsanity.com/paper/2504.07904/) (2025)
- [Hierarchy-Aware and Anatomy-Guided Learning for Lung Ultrasound Video Classification](https://mlsanity.com/paper/2607.17551/) (2026)
- [ZAGNet: Zone-Aware Graph Aggregation Network for Patient-Level Lung Ultrasound Diagnosis](https://mlsanity.com/paper/2610.02263/) (2026)

## Abstract (arXiv)

Self-supervised learning (SSL) methods have shown promise for medical imaging applications by learning meaningful visual representations, even when the amount of labeled data is limited. Here, we extend state-of-the-art contrastive learning SSL methods to 2D+time medical ultrasound video data by introducing a modified encoder and augmentation method capable of learning meaningful spatio-temporal representations, without requiring constraints on the input data. We evaluate our method on the challenging clinical task of identifying lung consolidations (an important pathological feature) in ultrasound videos. Using a multi-center dataset of over 27k lung ultrasound videos acquired from over 500 patients, we show that our method can significantly improve performance on downstream localization and classification of lung consolidation. Comparisons against baseline models trained without SSL show that the proposed methods are particularly advantageous when the size of labeled training data is limited (e.g., as little as 5% of the training set).