In clinical practice, well-aligned multi-modal images, such as Magnetic
Resonance (MR) and Computed Tomography (CT), together can provide complementary
information for image-guided therapies. Multi-modal image registration is
essential for the accurate alignment of these multi-modal images. However, it
remains a very challenging task due to complicated and unknown spatial
correspondence between different modalities. In this paper, we propose a novel
translation-based unsupervised deformable image registration approach to
convert the multi-modal registration problem to a mono-modal one. Specifically,
our approach incorporates a discriminator-free translation network to
facilitate the training of the registration network and a patchwise contrastive
loss to encourage the translation network to preserve object shapes.
Furthermore, we propose to replace an adversarial loss, that is widely used in
previous multi-modal image registration methods, with a pixel loss in order to
integrate the output of translation into the target modality. This leads to an
unsupervised method requiring no ground-truth deformation or pairs of aligned
images for training. We evaluate four variants of our approach on the public
Learn2Reg 2021 datasets \cite{hering2021learn2reg}. The experimental results
demonstrate that the proposed architecture achieves state-of-the-art
performance. Our code is available at this https URL

实现多模态图像的快速自动配准是一项具有挑战性的任务。本文提出了一种用于无监督多变形图像配准的新型基于翻译的方法，其利用无 diskriminator 的条件翻译网络来单调地转换多模态图像到单模态空间中，并采用位块对比度损失以保留物体形态，进而取代了对抗性损失以提高匹配的效率。在公共数据集上的实验结果表明该算法具有最先进的性能。

无监督多模态医学图像配准：基于无判别器图像转换的方法

Unsupervised Multi-Modal Medical Image Registration via  Discriminator-Free Image-to-Image Translation

Cross-lingual adaptation with multilingual pre-trained language models
(mPTLMs) mainly consists of two lines of works: zero-shot approach and
translation-based approach, which have been studied extensively on the
sequence-level tasks. We further verify the efficacy of these cross-lingual
adaptation approaches by evaluating their performances on more fine-grained
sequence tagging tasks. After re-examining their strengths and drawbacks, we
propose a novel framework to consolidate the zero-shot approach and the
translation-based approach for better adaptation performance. Instead of simply
augmenting the source data with the machine-translated data, we tailor-make a
warm-up mechanism to quickly update the mPTLMs with the gradients estimated on
a few translated data. Then, the adaptation approach is applied to the refined
parameters and the cross-lingual transfer is performed in a warm-start way. The
experimental results on nine target languages demonstrate that our method is
beneficial to the cross-lingual adaptation of various sequence tagging tasks.

研究了零样本学习和基于翻译的方法在预训练语言模型（mPTLMs）的跨语言适应性中的应用，提出了一种新的框架来整合这两种方法，通过定制一个预热机制来快速更新 mPTLMs，应用于细化的参数上进行跨语言适应性的实验结果表明，我们的方法有助于各种序列标注任务的跨语言适应性。