Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation. However, the pairing of Transformers with convolutional blocks in varying architectural permutations leaves their relative effectiveness to open interpretation. We introduce Transformer Ablations that replace the Transformer blocks with plain linear operators to quantify this effectiveness. With experiments on 8 models on 2 medical image segmentation tasks, we explore -- 1) the replaceable nature of Transformer-learnt representations, 2) Transformer capacity alone cannot prevent representational replaceability and works in tandem with effective design, 3) The mere existence of explicit feature hierarchies in transformer blocks is more beneficial than accompanying self-attention modules, 4) Major spatial downsampling before Transformer modules should be used with caution.

翻译：得益于在数据丰富的自然图像领域的成功，Transformer近年来在医学图像分割中变得流行。然而，Transformer与卷积模块在不同架构组合中的配对，使得其相对有效性存在开放式解读空间。我们引入Transformer消融实验，用普通线性算子替换Transformer模块以量化这种有效性。通过在2个医学图像分割任务上对8个模型进行实验，我们探究：1) Transformer所学表示的可替换性；2) Transformer容量本身无法阻止表示的可替换性，需与有效设计协同作用；3) Transformer模块中显式特征层次的存在本身比伴随的自注意力模块更有益；4) 应在Transformer模块前谨慎使用主要空间下采样。