MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
arxiv(2023)
摘要
There has been exploding interest in embracing Transformer-based
architectures for medical image segmentation. However, the lack of large-scale
annotated medical datasets make achieving performances equivalent to those in
natural images challenging. Convolutional networks, in contrast, have higher
inductive biases and consequently, are easily trainable to high performance.
Recently, the ConvNeXt architecture attempted to modernize the standard ConvNet
by mirroring Transformer blocks. In this work, we improve upon this to design a
modernized and scalable convolutional architecture customized to challenges of
data-scarce medical settings. We introduce MedNeXt, a Transformer-inspired
large kernel segmentation network which introduces - 1) A fully ConvNeXt 3D
Encoder-Decoder Network for medical image segmentation, 2) Residual ConvNeXt up
and downsampling blocks to preserve semantic richness across scales, 3) A novel
technique to iteratively increase kernel sizes by upsampling small kernel
networks, to prevent performance saturation on limited medical data, 4)
Compound scaling at multiple levels (depth, width, kernel size) of MedNeXt.
This leads to state-of-the-art performance on 4 tasks on CT and MRI modalities
and varying dataset sizes, representing a modernized deep architecture for
medical image segmentation. Our code is made publicly available at:
https://github.com/MIC-DKFZ/MedNeXt.
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