  Dense prediction is a fundamental requirement for many medical vision tasks
such as medical image restoration, registration, and segmentation. The most
popular vision model, Convolutional Neural Networks (CNNs), has reached
bottlenecks due to the intrinsic locality of convolution operations. Recently,
transformers have been widely adopted for dense prediction for their capability
to capture long-range visual dependence. However, due to the high computational
complexity and large memory consumption of self-attention operations,
transformers are usually used at downsampled feature resolutions. Such usage
cannot effectively leverage the tissue-level textural information available
only at the full image resolution. This textural information is crucial for
medical dense prediction as it can differentiate the subtle human anatomy in
medical images. In this study, we hypothesize that Multi-layer Perceptrons
(MLPs) are superior alternatives to transformers in medical dense prediction
where tissue-level details dominate the performance, as MLPs enable long-range
dependence at the full image resolution. To validate our hypothesis, we develop
a full-resolution hierarchical MLP framework that uses MLPs beginning from the
full image resolution. We evaluate this framework with various MLP blocks on a
wide range of medical dense prediction tasks including restoration,
registration, and segmentation. Extensive experiments on six public
well-benchmarked datasets show that, by simply using MLPs at full resolution,
our framework outperforms its CNN and transformer counterparts and achieves
state-of-the-art performance on various medical dense prediction tasks.
