Shaken or Stirred? An Analysis of MetaFormer's Token Mixing for Medical Imaging
This work addresses the lack of analysis on token mixers for medical imaging, providing insights for researchers and practitioners in this domain, though it is incremental as it builds on existing MetaFormer concepts.
The authors conducted the first comprehensive study of token mixers within the MetaFormer architecture for medical imaging, evaluating them on classification and segmentation across eight datasets. They found that low-complexity token mixers like grouped convolutions suffice for classification and are essential for segmentation, with pretrained weights remaining effective despite domain gaps.
The generalization of the Transformer architecture via MetaFormer has reshaped our understanding of its success in computer vision. By replacing self-attention with simpler token mixers, MetaFormer provides strong baselines for vision tasks. However, while extensively studied on natural image datasets, its use in medical imaging remains scarce, and existing works rarely compare different token mixers, potentially overlooking more suitable designs choices. In this work, we present the first comprehensive study of token mixers for medical imaging. We systematically analyze pooling-, convolution-, and attention-based token mixers within the MetaFormer architecture on image classification (global prediction task) and semantic segmentation (dense prediction task). Our evaluation spans eight datasets covering diverse modalities and common challenges in the medical domain. Given the prevalence of pretraining from natural images to mitigate medical data scarcity, we also examine transferring pretrained weights to new token mixers. Our results show that, for classification, low-complexity token mixers (e.g. grouped convolution or pooling) are sufficient, aligning with findings on natural images. Pretrained weights remain useful despite the domain gap introduced by the new token mixer. For segmentation, we find that the local inductive bias of convolutional token mixers is essential. Grouped convolutions emerge as the preferred choice, as they reduce runtime and parameter count compared to standard convolutions, while the MetaFormer's channel-MLPs already provide the necessary cross-channel interactions.