mlsanityData to 14 Oct 2026

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

arXiv 2403.07715 ↗First posted 12 Mar 2024eess.IV cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

Self-supervised learning (SSL) is one strategy for addressing the paucity of labelled data in medical imaging by learning representations from unlabelled images. Contrastive and non-contrastive SSL methods produce learned representations that are similar for pairs of related images. Such pairs are commonly constructed by randomly distorting the same image twice. The videographic nature of ultrasound offers flexibility for defining the similarity relationship between pairs of images. In this study, we investigated the effect of utilizing proximal, distinct images from the same B-mode ultrasound video as pairs for 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%. Detailed investigations of IVPP's hyperparameters revealed that some combinations of IVPP hyperparameters can lead to improved or worsened performance, depending on the downstream task. Guidelines for practitioners were synthesized based on the results, such as the merit of IVPP with task-specific hyperparameters, and the improved performance of contrastive methods for ultrasound compared to non-contrastive counterparts.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 28 of its 50 references that are in the map. First posted on arXiv 12 Mar 2024.

Builds on

28 of 50 references.

References · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  4. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  5. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  6. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750
  7. 2019Searching for MobileNetV3cited by 1,994
  8. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,838
  9. 2021VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learningcited by 1,057
  10. 2019Large Batch Optimization for Deep Learning: Training BERT in 76 minutescited by 674
  11. 2015Deep Metric Learning via Lifted Structured Feature Embeddingcited by 449
  12. 2021With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representationscited by 326
16 more
  1. 2021Big Self-Supervised Models Advance Medical Image Classificationcited by 320
  2. 2020Rethinking the Value of Labels for Improving Class-Imbalanced Learningcited by 216
  3. 2022Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methodscited by 149
  4. 2021Decoupled Contrastive Learningcited by 143
  5. 2021Self-supervised Learning is More Robust to Dataset Imbalancecited by 123
  6. 2022On the duality between contrastive and non-contrastive self-supervised learningcited by 108
  7. 2020USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learningcited by 34
  8. 2020POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)cited by 26
  9. 2022Self-supervised video pretraining yields robust and more human-aligned visual representationscited by 20
  10. 2023Towards Democratizing Joint-Embedding Self-Supervised Learningcited by 18
  11. 2017Neural Decision Treescited by 17
  12. 2022Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative Miningcited by 14
  13. 2022HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretrainingcited by 7
  14. 2022Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learningcited by 5
  15. 2023Self-Supervised Pretraining Improves Performance and Inference Efficiency in Multiple Lung Ultrasound Interpretation Taskscited by 3
  16. 2023Exploring the Utility of Self-Supervised Pretraining Strategies for the Detection of Absent Lung Sliding in M-Mode Lung Ultrasoundcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026Anatomy-Anchored Self-Supervision: Distilling Vision Foundation Models for Invariant Ultrasound Representationcited by 2
  2. 2025The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasoundcited by 0