The advent of deep-learning-based registration networks has addressed the
time-consuming challenge in traditional iterative methods.However, the
potential of current registration networks for comprehensively capturing
spatial relationships has not been fully explored, leading to inadequate
performance in large-deformation image registration.The pure convolutional
neural networks (CNNs) neglect feature enhancement, while current
Transformer-based networks are susceptible to information redundancy.To
alleviate these issues, we propose a pyramid attention network (PAN) for
deformable medical image registration.Specifically, the proposed PAN
incorporates a dual-stream pyramid encoder with channel-wise attention to boost
the feature representation.Moreover, a multi-head local attention Transformer
is introduced as decoder to analyze motion patterns and generate deformation
fields.Extensive experiments on two public brain magnetic resonance imaging
(MRI) datasets and one abdominal MRI dataset demonstrate that our method
achieves favorable registration performance, while outperforming several
CNN-based and Transformer-based registration networks.Our code is publicly
available at this https URL

我们提出了金字塔注意力网络（PAN）用于可变形医学图像配准，通过引入双流金字塔编码器和局部多头注意力变换器实现特征表示和分析运动模式，取得了较好的配准性能。

金字塔注意力网络用于医学图像配准

Pyramid Attention Network for Medical Image Registration

The nature of deep neural networks has given rise to a variety of attacks,
but little work has been done to address the effect of adversarial attacks on
segmentation models trained on MRI datasets. In light of the grave consequences
that such attacks could cause, we explore four models from the U-Net family and
examine their responses to the Fast Gradient Sign Method (FGSM) attack. We
conduct FGSM attacks on each of them and experiment with various schemes to
conduct the attacks. In this paper, we find that medical imaging segmentation
models are indeed vulnerable to adversarial attacks and that there is a
negligible correlation between parameter size and adversarial attack success.
Furthermore, we show that using a different loss function than the one used for
training yields higher adversarial attack success, contrary to what the FGSM
authors suggested. In future efforts, we will conduct the experiments detailed
in this paper with more segmentation models and different attacks. We will also
attempt to find ways to counteract the attacks by using model ensembles or
special data augmentations. Our code is available at
this https URL

医学影像分割模型容易受到对抗性攻击，参数大小与对抗性攻击成功率之间没有明显的相关性，并使用不同于训练所用的损失函数可提高对抗性攻击的成功率。

医学图像分割模型对对抗攻击的敏感性

Susceptibility of Adversarial Attack on Medical Image Segmentation  Models

The scarcity of pixel-level annotation is a prevalent problem in medical
image segmentation tasks. In this paper, we introduce a novel regularization
strategy involving interpolation-based mixing for semi-supervised medical image
segmentation. The proposed method is a new consistency regularization strategy
that encourages segmentation of interpolation of two unlabelled data to be
consistent with the interpolation of segmentation maps of those data. This
method represents a specific type of data-adaptive regularization paradigm
which aids to minimize the overfitting of labelled data under high confidence
values. The proposed method is advantageous over adversarial and generative
models as it requires no additional computation. Upon evaluation on two
publicly available MRI datasets: ACDC and MMWHS, experimental results
demonstrate the superiority of the proposed method in comparison to existing
semi-supervised models. Code is available at:
this https URL

本文提出了一种新颖的正则化策略，包括基于插值的混合策略，以此来解决医学图像分割任务中像素级注释不足的普遍问题。实验证明该方法比现有的半监督模型更具优越性，且不需要额外的计算。