The recent vision transformer(i.e.for image classification) learns non-local
attentive interaction of different patch tokens. However, prior arts miss
learning the cross-scale dependencies of different pixels, the semantic
correspondence of different labels, and the consistency of the feature
representations and semantic embeddings, which are critical for biomedical
segmentation. In this paper, we tackle the above issues by proposing a unified
transformer network, termed Multi-Compound Transformer (MCTrans), which
incorporates rich feature learning and semantic structure mining into a unified
framework. Specifically, MCTrans embeds the multi-scale convolutional features
as a sequence of tokens and performs intra- and inter-scale self-attention,
rather than single-scale attention in previous works. In addition, a learnable
proxy embedding is also introduced to model semantic relationship and feature
enhancement by using self-attention and cross-attention, respectively. MCTrans
can be easily plugged into a UNet-like network and attains a significant
improvement over the state-of-the-art methods in biomedical image segmentation
in six standard benchmarks. For example, MCTrans outperforms UNet by 3.64%,
3.71%, 4.34%, 2.8%, 1.88%, 1.57% in Pannuke, CVC-Clinic, CVC-Colon, Etis,
Kavirs, ISIC2018 dataset, respectively. Code is available at
this https URL

本研究提出了一种统一的变形器网络，称为多化合物变形器（MCTrans），可嵌入 UNet-like 网络中，并在生物医学图像分割中显着提高了最先进方法的性能。它使用自我关注和交叉注意力进行多尺度卷积特征的序列嵌入，引入了可学习的代理嵌入来模拟语义关系和特征增强。

用于精确生物医学图像分割的多化合物转换器

Multi-Compound Transformer for Accurate Biomedical Image Segmentation

CNN-based volumetric methods that label individual voxels now dominate the
field of biomedical segmentation. However, 3D surface representations are often
required for proper analysis. They can be obtained by post-processing the
labeled volumes which typically introduces artifacts and prevents end-to-end
training. In this paper, we therefore introduce a novel architecture that goes
directly from 3D image volumes to 3D surfaces without post-processing and with
better accuracy than current methods. We evaluate it on Electron Microscopy and
MRI brain images as well as CT liver scans. We will show that it outperforms
state-of-the-art segmentation methods.

本研究介绍了一种新的体系结构，可实现直接从三维图像体积转换为三维表面，无需后处理并具有比当前方法更好的准确性，证明其优于最先进的分割方法。

基于体素数据的三维网格模型生成

Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data

\begin{abstract} Learning-based methods suffer from a deficiency of clean
annotations, especially in biomedical segmentation. Although many
semi-supervised methods have been proposed to provide extra training data,
automatically generated labels are usually too noisy to retrain models
effectively. In this paper, we propose a Two-Stream Mutual Attention Network
(TSMAN) that weakens the influence of back-propagated gradients caused by
incorrect labels, thereby rendering the network robust to unclean data. The
proposed TSMAN consists of two sub-networks that are connected by three types
of attention models in different layers. The target of each attention model is
to indicate potentially incorrect gradients in a certain layer for both
sub-networks by analyzing their inferred features using the same input. In
order to achieve this purpose, the attention models are designed based on the
propagation analysis of noisy gradients at different layers. This allows the
attention models to effectively discover incorrect labels and weaken their
influence during the parameter updating process. By exchanging multi-level
features within the two-stream architecture, the effects of noisy labels in
each sub-network are reduced by decreasing the updating gradients. Furthermore,
a hierarchical distillation is developed to provide more reliable pseudo labels
for unlabelded data, which further boosts the performance of our retrained
TSMAN. The experiments using both the HVSMR 2016 and BRATS 2015 benchmarks
demonstrate that our semi-supervised learning framework surpasses the
state-of-the-art fully-supervised results.

本文提出了一个双流互注意网络 (TSMAN) 来弱化错误标签的反向传播梯度的影响，从而使神经网络在处理不干净的数据时更加稳健。使用半监督学习框架，通过交换双流结构内的多级特征来降低每个子网络中嘈杂标签的影响，并进一步优化性能。在 HVSMR2016 和 BRATS2015 基准测试中的实验表明，所提出的方法能够超越目前的全监督结果。