Representation Learning for 3D Brain Imaging: A Benchmark

원문 초록

Many deep learning architectures for computer vision tasks rely on features extracted by a pre-trained encoder on large databases of natural 2D RGB images. However, the wide domain gap with medical images hampers the direct transfer of optimized deep learning models from computer vision to clinical applications. Recently, self-supervised pre-training and foundation models emerged as promising alternatives to mitigate these limitations. Yet, constructing such models for medical imaging faces critical challenges, including designing and adapting training strategies for large 3D volume datasets, and the need for standardized benchmarks to evaluate feature extraction performance. In this study, we propose an extensive comparison of vision encoders with a focus on 3D brain MRI. First, we consider 8 fully and self-supervised models pretrained on natural images (e.g. ResNet, DINO, CLIP) and 10 backbone architectures trained on medical images, either based on supervised (e.g. RadImageNet) or self-supervised (e.g. BiomedCLIP, SimCLR) tasks. All pre-trained encoders are evaluated on various downstream tasks to assess the representation power of their latent representations, including age and sex prediction as well as disease classification on the ADNI and PPMI datasets. Our study shows that 2D encoders pre-trained on natural images perform the most favorably, even compared to models pre-trained on more than 100k brain MRI volumes, highlighting the need for more suitable and robust methods for representation learning in 3D medical imaging. The source code for this study is publicly available at: https: //github.com/PierreFalconnier/brain-representation-benchmark.