Revisiting MAE pre-training for 3D medical image segmentation
Keywords: self-supervised learning, medical image segmentation, foundation models, medical image computing, CNN, nnU-Net
TL;DR: We revisit MAE pretraining for 3D medical image segmentation for state-of-the-art CNNs and show it's efficacy relative to currently established SSL methods in the medical domain.
Abstract: Self-Supervised Learning (SSL) presents an exciting opportunity to unlock the potential of vast, untapped clinical datasets, for various downstream applications that suffer from the scarcity of labeled data. While SSL has revolutionized fields like natural language processing and computer vision, their adoption in 3D medical image computing has been limited by three key pitfalls: Small pre-training
dataset sizes, architectures inadequate for 3D medical image analysis, and insufficient evaluation practices. We address these issues by i) leveraging a large-scale dataset of 44k 3D brain MRI volumes and ii) using a Residual Encoder U-Net architecture within the state-of-the-art nnU-Net framework. iii) A robust development framework, incorporating 5 development and 8 testing brain MRI segmentation datasets, allowed performance-driven design decisions to optimize the simple concept of Masked Auto Encoders (MAEs) for 3D CNNs. The resulting model not only surpasses previous SSL methods but also outperforms the strong nnU-Net baseline by an average of approximately 3 Dice points. Furthermore, our model demonstrates exceptional stability, achieving the highest average rank of 2 out of 7 methods, compared to the second-best method’s mean rank of 3. Our code is made available here.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 7147
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