# VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis

arXiv: https://arxiv.org/abs/2402.17300 · HTML: https://mlsanity.com/paper/2402.17300/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2024.

## Built on by (most cited first, 30 of 75)

- [Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions](https://mlsanity.com/paper/2404.03264/) (2024)
- [Large-Scale 3D Medical Image Pre-training with Geometric Context Priors](https://mlsanity.com/paper/2410.09890/) (2024)
- [Revisiting MAE pre-training for 3D medical image segmentation](https://mlsanity.com/paper/2410.23132/) (2024)
- [An OpenMind for 3D medical vision self-supervised learning](https://mlsanity.com/paper/2412.17041/) (2024)
- [Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging](https://mlsanity.com/paper/2502.14064/) (2025)
- [Multi-modal Vision Pre-training for Medical Image Analysis](https://mlsanity.com/paper/2410.10604/) (2024)
- [Comprehensive language-image pre-training for 3D medical image understanding](https://mlsanity.com/paper/2510.15042/) (2025)
- [MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis](https://mlsanity.com/paper/2404.15580/) (2024)
- [Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2412.02621/) (2024)
- [FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis](https://mlsanity.com/paper/2406.01264/) (2024)
- [Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model](https://mlsanity.com/paper/2411.12783/) (2024)
- [UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation](https://mlsanity.com/paper/2504.21336/) (2025)
- [Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification](https://mlsanity.com/paper/2512.12887/) (2025)
- [Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Review](https://mlsanity.com/paper/2502.14886/) (2025)
- [DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2504.04566/) (2025)
- [CT-GRAPH: Hierarchical Graph Attention Network for Anatomy-Guided CT Report Generation](https://mlsanity.com/paper/2508.05375/) (2025)
- [MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model](https://mlsanity.com/paper/2410.05905/) (2024)
- [Does DINOv3 Set a New Medical Vision Standard? Benchmarking 2D and 3D Classification, Segmentation, and Registration](https://mlsanity.com/paper/2509.06467/) (2025)
- [Scaling Self-Supervised and Cross-Modal Pretraining for Volumetric CT Transformers](https://mlsanity.com/paper/2511.17209/) (2025)
- [Diffuse-UDA: Addressing Unsupervised Domain Adaptation in Medical Image Segmentation with Appearance and Structure Aligned Diffusion Models](https://mlsanity.com/paper/2408.05985/) (2024)
- [Structure-aware Semantic Discrepancy and Consistency for 3D Medical Image Self-supervised Learning](https://mlsanity.com/paper/2507.02581/) (2025)
- [Towards a Universal 3D Medical Multi-modality Generalization via Learning Personalized Invariant Representation](https://mlsanity.com/paper/2411.06106/) (2024)
- [Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision](https://mlsanity.com/paper/2506.20850/) (2025)
- [Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation](https://mlsanity.com/paper/2507.06363/) (2025)
- [Adaptation of Foundation Models for Medical Image Analysis: Strategies, Challenges, and Future Directions](https://mlsanity.com/paper/2511.01284/) (2025)
- [Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge](https://mlsanity.com/paper/2604.11679/) (2026)
- [Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging](https://mlsanity.com/paper/2503.19005/) (2025)
- [Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approaches](https://mlsanity.com/paper/2506.10825/) (2025)
- [VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine](https://mlsanity.com/paper/2508.12108/) (2025)
- [M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision](https://mlsanity.com/paper/2509.01360/) (2025)

## Abstract (arXiv)

Self-Supervised Learning (SSL) has demonstrated promising results in 3D medical image analysis. However, the lack of high-level semantics in pre-training still heavily hinders the performance of downstream tasks. We observe that 3D medical images contain relatively consistent contextual position information, i.e., consistent geometric relations between different organs, which leads to a potential way for us to learn consistent semantic representations in pre-training. In this paper, we propose a simple-yet-effective Volume Contrast (VoCo) framework to leverage the contextual position priors for pre-training. Specifically, we first generate a group of base crops from different regions while enforcing feature discrepancy among them, where we employ them as class assignments of different regions. Then, we randomly crop sub-volumes and predict them belonging to which class (located at which region) by contrasting their similarity to different base crops, which can be seen as predicting contextual positions of different sub-volumes. Through this pretext task, VoCo implicitly encodes the contextual position priors into model representations without the guidance of annotations, enabling us to effectively improve the performance of downstream tasks that require high-level semantics. Extensive experimental results on six downstream tasks demonstrate the superior effectiveness of VoCo. Code will be available at https://github.com/Luffy03/VoCo.