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.

翻译：近年来，基于Transformer架构的医学图像分割方法引发了极大关注。然而，大规模标注医学数据集的匮乏使得其难以达到自然图像领域的性能水平。相比之下，卷积网络具有更强的归纳偏置，因而更易训练至高性能。近期，ConvNeXt架构通过模仿Transformer模块尝试对标准卷积网络进行现代化改造。本研究在此基础上，针对医学领域数据稀缺的挑战，设计了一种现代化、可扩展的定制化卷积架构。我们提出的MedNeXt是一种受Transformer启发的大核分割网络，其创新点包括：1）全ConvNeXt三维编码器-解码器网络用于医学图像分割；2）残差ConvNeXt上下采样模块以保持跨尺度的语义丰富性；3）通过上采样小核网络逐步扩大卷积核尺寸的新技术，避免在有限医学数据上出现性能饱和；4）MedNeXt多层级（深度、宽度、核尺寸）复合缩放策略。该架构在CT与MRI模态的4项任务及不同规模数据集上均达到最优性能，代表了医学图像分割领域现代化的深度学习架构。