# USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning

arXiv: https://arxiv.org/abs/2011.13066 · HTML: https://mlsanity.com/paper/2011.13066/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 30 of 34)

- [Data-free Backdoor Removal based on Channel Lipschitzness](https://mlsanity.com/paper/2208.03111/) (2022)
- [A Comprehensive Survey on Segment Anything Model for Vision and Beyond](https://mlsanity.com/paper/2305.08196/) (2023)
- [Dissecting Self-Supervised Learning Methods for Surgical Computer Vision](https://mlsanity.com/paper/2207.00449/) (2022)
- [Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks](https://mlsanity.com/paper/2410.18387/) (2024)
- [USF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencoding](https://mlsanity.com/paper/2510.22990/) (2025)
- [Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative Mining](https://mlsanity.com/paper/2207.13148/) (2022)
- [Metadata-enhanced contrastive learning from retinal optical coherence tomography images](https://mlsanity.com/paper/2208.02529/) (2022)
- [TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes](https://mlsanity.com/paper/2206.15282/) (2022)
- [RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer Detection](https://mlsanity.com/paper/2211.04793/) (2022)
- [Anatomy-Aware Contrastive Representation Learning for Fetal Ultrasound](https://mlsanity.com/paper/2208.10642/) (2022)
- [HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining](https://mlsanity.com/paper/2210.04477/) (2022)
- [Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation](https://mlsanity.com/paper/2203.03664/) (2022)
- [Unveiling and Mitigating Backdoor Vulnerabilities based on Unlearning Weight Changes and Backdoor Activeness](https://mlsanity.com/paper/2405.20291/) (2024)
- [Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning](https://mlsanity.com/paper/2212.04097/) (2022)
- [Joint semi-supervised and contrastive learning enables domain generalization and multi-domain segmentation](https://mlsanity.com/paper/2405.05336/) (2024)
- [MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging](https://mlsanity.com/paper/2206.01408/) (2022)
- [Masked Video Modeling with Correlation-aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasound](https://mlsanity.com/paper/2208.09881/) (2022)
- [Rethinking Two Consensuses of the Transferability in Deep Learning](https://mlsanity.com/paper/2212.00399/) (2022)
- [Self-Supervised Pretraining Improves Performance and Inference Efficiency in Multiple Lung Ultrasound Interpretation Tasks](https://mlsanity.com/paper/2309.02596/) (2023)
- [Gall Bladder Cancer Detection from US Images with Only Image Level Labels](https://mlsanity.com/paper/2309.05261/) (2023)
- [3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs](https://mlsanity.com/paper/2312.16980/) (2023)
- [Breast Lesion Diagnosis Using Static Images and Dynamic Video](https://mlsanity.com/paper/2308.09980/) (2023)
- [Intra-video Positive Pairs in Self-Supervised Learning for Ultrasound](https://mlsanity.com/paper/2403.07715/) (2024)
- [COST: Contrastive One-Stage Transformer for Vision-Language Small Object Tracking](https://mlsanity.com/paper/2504.01321/) (2025)
- [Anatomy-Anchored Self-Supervision: Distilling Vision Foundation Models for Invariant Ultrasound Representation](https://mlsanity.com/paper/2605.25402/) (2026)
- [Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning](https://mlsanity.com/paper/2306.03835/) (2023)
- [Auto-US: An Ultrasound Video Diagnosis Agent Using Video Classification Framework and LLMs](https://mlsanity.com/paper/2511.07748/) (2025)
- [Spatio-Temporal Structure Consistency for Semi-supervised Medical Image Classification](https://mlsanity.com/paper/2303.01707/) (2023)
- [Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT](https://mlsanity.com/paper/2304.08439/) (2023)
- [Automatic retrieval of corresponding US views in longitudinal examinations](https://mlsanity.com/paper/2306.04739/) (2023)

## Abstract (arXiv)

Most deep neural networks (DNNs) based ultrasound (US) medical image analysis models use pretrained backbones (e.g., ImageNet) for better model generalization. However, the domain gap between natural and medical images causes an inevitable performance bottleneck. To alleviate this problem, an US dataset named US-4 is constructed for direct pretraining on the same domain. It contains over 23,000 images from four US video sub-datasets. To learn robust features from US-4, we propose an US semi-supervised contrastive learning method, named USCL, for pretraining. In order to avoid high similarities between negative pairs as well as mine abundant visual features from limited US videos, USCL adopts a sample pair generation method to enrich the feature involved in a single step of contrastive optimization. Extensive experiments on several downstream tasks show the superiority of USCL pretraining against ImageNet pretraining and other state-of-the-art (SOTA) pretraining approaches. In particular, USCL pretrained backbone achieves fine-tuning accuracy of over 94% on POCUS dataset, which is 10% higher than 84% of the ImageNet pretrained model. The source codes of this work are available at https://github.com/983632847/USCL.