mlsanityData to 14 Oct 2026

HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining

arXiv 2210.04477 ↗First posted 10 Oct 2022cs.CVcited by 7

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Abstract (arXiv)

The self-supervised ultrasound (US) video model pretraining can use a small amount of labeled data to achieve one of the most promising results on US diagnosis. However, it does not take full advantage of multi-level knowledge for learning deep neural networks (DNNs), and thus is difficult to learn transferable feature representations. This work proposes a hierarchical contrastive learning (HiCo) method to improve the transferability for the US video model pretraining. HiCo introduces both peer-level semantic alignment and cross-level semantic alignment to facilitate the interaction between different semantic levels, which can effectively accelerate the convergence speed, leading to better generalization and adaptation of the learned model. Additionally, a softened objective function is implemented by smoothing the hard labels, which can alleviate the negative effect caused by local similarities of images between different classes. Experiments with HiCo on five datasets demonstrate its favorable results over state-of-the-art approaches. The source code of this work is publicly available at https://github.com/983632847/HiCo.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 35 of its 61 references that are in the map. First posted on arXiv 10 Oct 2022.

Builds on

35 of 61 references.

References · 35

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23 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 2; 2026: 1.

7 papers.

  1. 2023A Comprehensive Survey on Segment Anything Model for Vision and Beyondcited by 64
  2. 2022Rethinking Two Consensuses of the Transferability in Deep Learningcited by 3
  3. 2024Intra-video Positive Pairs in Self-Supervised Learning for Ultrasoundcited by 2
  4. 2025COST: Contrastive One-Stage Transformer for Vision-Language Small Object Trackingcited by 2
  5. 2024Leveraging Self-Supervised Learning for Fetal Cardiac Planes Classification using Ultrasound Scan Videoscited by 0
  6. 2025The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasoundcited by 0
  7. 2026Hierarchy-Aware and Anatomy-Guided Learning for Lung Ultrasound Video Classificationcited by 0