# Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder

arXiv: https://arxiv.org/abs/2306.08913 · HTML: https://mlsanity.com/paper/2306.08913/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 70 of its 97 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 30 of 70)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [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)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (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)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [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)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Self-labelling via simultaneous clustering and representation learning](https://mlsanity.com/paper/1911.05371/) (2019)

## Built on by (most cited first, 21 of 21)

- [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)
- [How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model](https://mlsanity.com/paper/2404.09957/) (2024)
- [An OpenMind for 3D medical vision self-supervised learning](https://mlsanity.com/paper/2412.17041/) (2024)
- [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)
- [Scaling Artificial Intelligence for Multi-Tumor Early Detection with More Reports, Fewer Masks](https://mlsanity.com/paper/2510.14803/) (2025)
- [Applications of Large Models in Medicine](https://mlsanity.com/paper/2502.17132/) (2025)
- [Structure-aware Semantic Discrepancy and Consistency for 3D Medical Image Self-supervised Learning](https://mlsanity.com/paper/2507.02581/) (2025)
- [VasoMIM: Vascular Anatomy-Aware Masked Image Modeling for Vessel Segmentation](https://mlsanity.com/paper/2508.10794/) (2025)
- [Towards Generalist Intelligence in Dentistry: Vision Foundation Models for Oral and Maxillofacial Radiology](https://mlsanity.com/paper/2510.14532/) (2025)
- [MedCL: Learning Consistent Anatomy Distribution for Scribble-supervised Medical Image Segmentation](https://mlsanity.com/paper/2503.22890/) (2025)
- [Learning from Random Subspace Exploration: Generalized Test-Time Augmentation with Self-supervised Distillation](https://mlsanity.com/paper/2507.01347/) (2025)
- [MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis](https://mlsanity.com/paper/2602.17901/) (2026)
- [Anatomically-guided masked autoencoder pre-training for aneurysm detection](https://mlsanity.com/paper/2502.21244/) (2025)
- [ModAn-MulSupCon: Modality-and Anatomy-Aware Multi-Label Supervised Contrastive Pretraining for Medical Imaging](https://mlsanity.com/paper/2508.18613/) (2025)
- [FDIF: Formula-Driven supervised Learning with Implicit Functions for 3D Medical Image Segmentation](https://mlsanity.com/paper/2603.23199/) (2026)
- [Adaptive Texture-aware Masking for Self-Supervised Learning in 3D Dental CBCT Analysis](https://mlsanity.com/paper/2605.01741/) (2026)
- [Curia-MAE: Multi-Modal Multi-Anatomy MAE Pre-Training for 3D Medical Image Segmentation](https://mlsanity.com/paper/2608.05844/) (2026)
- [nnFoundation: 3D Foundation Models for Radiology](https://mlsanity.com/paper/2609.26924/) (2026)
- [Where to Look Matters: Rethinking Sub-Volume Sampling in 3D Medical Self-supervised Learning](https://mlsanity.com/paper/2610.13915/) (2026)

## Abstract (arXiv)

Masked autoencoder (MAE) is a promising self-supervised pre-training technique that can improve the representation learning of a neural network without human intervention. However, applying MAE directly to volumetric medical images poses two challenges: (i) a lack of global information that is crucial for understanding the clinical context of the holistic data, (ii) no guarantee of stabilizing the representations learned from randomly masked inputs. To address these limitations, we propose the Global-Local Masked AutoEncoder (GL-MAE), a simple yet effective self-supervised pre-training strategy. In addition to reconstructing masked local views, as in previous methods, GL-MAE incorporates global context learning by reconstructing masked global views. Furthermore, a complete global view is integrated as an anchor to guide the reconstruction and stabilize the learning process through global-to-global consistency learning and global-to-local consistency learning. Finetuning results on multiple datasets demonstrate the superiority of our method over other state-of-the-art self-supervised algorithms, highlighting its effectiveness on versatile volumetric medical image segmentation tasks, even when annotations are scarce. Our codes and models will be released upon acceptance.