# Large-Scale 3D Medical Image Pre-training with Geometric Context Priors

arXiv: https://arxiv.org/abs/2410.09890 · HTML: https://mlsanity.com/paper/2410.09890/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 88 of its 162 references that are in the map.
- First posted on arXiv 13 Oct 2024.

## Builds on (most cited first, 30 of 88)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [iBOT: Image BERT Pre-Training with Online Tokenizer](https://mlsanity.com/paper/2111.07832/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [EVA: Exploring the Limits of Masked Visual Representation Learning at Scale](https://mlsanity.com/paper/2211.07636/) (2022)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)

## Built on by (most cited first, 30 of 36)

- [Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging](https://mlsanity.com/paper/2502.14064/) (2025)
- [Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models](https://mlsanity.com/paper/2507.08254/) (2025)
- [UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation](https://mlsanity.com/paper/2504.21336/) (2025)
- [Efficient Chest X-ray Representation Learning via Semantic-Partitioned Contrastive Learning](https://mlsanity.com/paper/2603.07113/) (2026)
- [Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification](https://mlsanity.com/paper/2512.12887/) (2025)
- [MG-3D: Multi-Grained Knowledge-Enhanced 3D Medical Vision-Language Pre-training](https://mlsanity.com/paper/2412.05876/) (2024)
- [CT-GRAPH: Hierarchical Graph Attention Network for Anatomy-Guided CT Report Generation](https://mlsanity.com/paper/2508.05375/) (2025)
- [Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation](https://mlsanity.com/paper/2508.09177/) (2025)
- [Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining](https://mlsanity.com/paper/2605.21906/) (2026)
- [Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision](https://mlsanity.com/paper/2506.20850/) (2025)
- [M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision](https://mlsanity.com/paper/2509.01360/) (2025)
- [GreenRFM: Learning a resource-efficient radiology vision-language foundation model via supervision-centric pre-training](https://mlsanity.com/paper/2603.06467/) (2026)
- [Learning Clinical Representations Under Systematic Distribution Shift](https://mlsanity.com/paper/2603.07348/) (2026)
- [Discriminative Representation Learning for Clinical Prediction](https://mlsanity.com/paper/2603.20921/) (2026)
- [CoralBay: A Self-Supervised CT Foundation Model](https://mlsanity.com/paper/2606.03888/) (2026)
- [Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning](https://mlsanity.com/paper/2501.12057/) (2025)
- [MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation](https://mlsanity.com/paper/2509.06096/) (2025)
- [HU-based Foreground Masking for 3D Medical Masked Image Modeling](https://mlsanity.com/paper/2509.07534/) (2025)
- [Glance and Focus Reinforcement for Pan-cancer Screening](https://mlsanity.com/paper/2601.19103/) (2026)
- [MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis](https://mlsanity.com/paper/2602.17901/) (2026)
- [Uncertainty-Aware Foundation Models for Clinical Data](https://mlsanity.com/paper/2604.04175/) (2026)
- [ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training](https://mlsanity.com/paper/2606.00602/) (2026)
- [Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translation](https://mlsanity.com/paper/2607.07219/) (2026)
- [MRN: Harnessing 2D Vision Foundation Models for Diagnosing Parkinson's Disease with Limited 3D MR Data](https://mlsanity.com/paper/2509.17566/) (2025)
- [Intuitions of Machine Learning Researchers about Transfer Learning for Medical Image Classification](https://mlsanity.com/paper/2510.00902/) (2025)
- [Vascular anatomy-aware self-supervised pre-training for X-ray angiogram analysis](https://mlsanity.com/paper/2602.11536/) (2026)
- [Free Lunch in Medical Image Foundation Model Pre-training via Randomized Synthesis and Disentanglement](https://mlsanity.com/paper/2602.12317/) (2026)
- [PlaneCycle: Training-Free 2D-to-3D Lifting of Foundation Models Without Adapters](https://mlsanity.com/paper/2603.04165/) (2026)
- [CORA: Generalizable coronary artery disease assessment and risk stratification from coronary CT angiography using pathology-centric representation learning](https://mlsanity.com/paper/2603.24847/) (2026)
- [Tumor-aware augmentation with task-guided attention analysis improves rectal cancer segmentation from magnetic resonance images](https://mlsanity.com/paper/2605.05522/) (2026)

## Abstract (arXiv)

The scarcity of annotations poses a significant challenge in medical image analysis. Large-scale pre-training has emerged as a promising label-efficient solution, owing to the utilization of large-scale data, large models, and advanced pre-training techniques. However, its development in medical images remains underexplored. The primary challenge lies in harnessing large-scale unlabeled data and learning high-level semantics without annotations. We observe that 3D medical images exhibit consistent geometric context, i.e., consistent geometric relations between different organs, which leads to a promising way for learning consistent representations. Motivated by this, we introduce a simple-yet-effective Volume Contrast (VoCo) framework to leverage geometric context priors for self-supervision. Given an input volume, we extract base crops from different regions to construct positive and negative pairs for contrastive learning. Then we predict the contextual position of a random crop by contrasting its similarity to the base crops. In this way, VoCo encodes the inherent geometric context into model representations, facilitating high-level semantic learning without annotations. Specifically, we (1) introduce the largest medical pre-training dataset PreCT-160K; (2) investigate scaling laws and propose guidelines for tailoring different model sizes to various medical tasks; (3) build a benchmark encompassing 48 medical tasks. Extensive experiments highlight the superiority of VoCo. Codes at https://github.com/Luffy03/Large-Scale-Medical.