Dense prediction in medical volume provides enriched guidance for clinical
analysis. CNN backbones have met bottleneck due to lack of long-range
dependencies and global context modeling power. Recent works proposed to
combine vision transformer with CNN, due to its strong global capture ability
and learning capability. However, most works are limited to simply applying
pure transformer with several fatal flaws (i.e., lack of inductive bias, heavy
computation and little consideration for 3D data). Therefore, designing an
elegant and efficient vision transformer learner for dense prediction in
medical volume is promising and challenging. In this paper, we propose a novel
3D Shuffle-Mixer network of a new Local Vision Transformer-MLP paradigm for
medical dense prediction. In our network, a local vision transformer block is
utilized to shuffle and learn spatial context from full-view slices of
rearranged volume, a residual axial-MLP is designed to mix and capture
remaining volume context in a slice-aware manner, and a MLP view aggregator is
employed to project the learned full-view rich context to the volume feature in
a view-aware manner. Moreover, an Adaptive Scaled Enhanced Shortcut is proposed
for local vision transformer to enhance feature along spatial and channel
dimensions adaptively, and a CrossMerge is proposed to skip-connects the
multi-scale feature appropriately in the pyramid architecture. Extensive
experiments demonstrate the proposed model outperforms other state-of-the-art
medical dense prediction methods.

研究提出了一种新的 3D Shuffle-Mixer 网络，它采用本地视觉变换器 - MLP 范例，可以更有效地应用于医学成像中的密集预测，获得比其他先进的密集预测方法更好的性能。

3D Shuffle-Mixer: 一种用于医疗体积稠密预测的 Transformer-MLP 范式的高效上下文感知视觉学习器

3D Shuffle-Mixer: An Efficient Context-Aware Vision Learner of Transformer-MLP Paradigm for Dense Prediction in Medical Volume

Vision Transformer (ViT) attains state-of-the-art performance in visual
recognition, and the variant, Local Vision Transformer, makes further
improvements. The major component in Local Vision Transformer, local attention,
performs the attention separately over small local windows. We rephrase local
attention as a channel-wise locally-connected layer and analyze it from two
network regularization manners, sparse connectivity and weight sharing, as well
as weight computation. Sparse connectivity: there is no connection across
channels, and each position is connected to the positions within a small local
window. Weight sharing: the connection weights for one position are shared
across channels or within each group of channels. Dynamic weight: the
connection weights are dynamically predicted according to each image instance.
We point out that local attention resembles depth-wise convolution and its
dynamic version in sparse connectivity. The main difference lies in weight
sharing - depth-wise convolution shares connection weights (kernel weights)
across spatial positions. We empirically observe that the models based on
depth-wise convolution and the dynamic variant with lower computation
complexity perform on-par with or sometimes slightly better than Swin
Transformer, an instance of Local Vision Transformer, for ImageNet
classification, COCO object detection and ADE semantic segmentation. These
observations suggest that Local Vision Transformer takes advantage of two
regularization forms and dynamic weight to increase the network capacity. Code
is available at this https URL

本文探讨了 Local Vision Transformer 的前身 ——Vision Transformer 和其一种局部注意力机制 —— 局部连接的形式，并从 network regularization 角度分析了其权重的稀疏性、共享性以及动态性。此外作者还针对这种局部连接进行的实验证明它的有效性，并提供了开源实现代码。