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There has been exploding interest in embracing Transformer-basedarchitectures for medical image segmentation. However, the lack of large-scaleannotated medical datasets make achieving performances equivalent to those innatural images challenging. Convolutional networks, in contrast, have higherinductive biases and consequently, are easily trainable to high performance.Recently, the ConvNeXt architecture attempted to modernize the standard ConvNetby mirroring Transformer blocks. In this work, we improve upon this to design amodernized and scalable convolutional architecture customized to challenges ofdata-scarce medical settings. We introduce MedNeXt, a Transformer-inspiredlarge kernel segmentation network which introduces - 1) A fully ConvNeXt 3DEncoder-Decoder Network for medical image segmentation, 2) Residual ConvNeXt upand downsampling blocks to preserve semantic richness across scales, 3) A noveltechnique to iteratively increase kernel sizes by upsampling small kernelnetworks, 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 modalitiesand varying dataset sizes, representing a modernized deep architecture formedical image segmentation. Our code is made publicly available at:https://github.com/MIC-DKFZ/MedNeXt.

MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation