Accurate medical image segmentation is of utmost importance for enabling
automated clinical decision procedures. However, prevailing supervised deep
learning approaches for medical image segmentation encounter significant
challenges due to their heavy dependence on extensive labeled training data. To
tackle this issue, we propose a novel self-supervised algorithm,
\textbf{S$^3$-Net}, which integrates a robust framework based on the proposed
Inception Large Kernel Attention (I-LKA) modules. This architectural
enhancement makes it possible to comprehensively capture contextual information
while preserving local intricacies, thereby enabling precise semantic
segmentation. Furthermore, considering that lesions in medical images often
exhibit deformations, we leverage deformable convolution as an integral
component to effectively capture and delineate lesion deformations for superior
object boundary definition. Additionally, our self-supervised strategy
emphasizes the acquisition of invariance to affine transformations, which is
commonly encountered in medical scenarios. This emphasis on robustness with
respect to geometric distortions significantly enhances the model's ability to
accurately model and handle such distortions. To enforce spatial consistency
and promote the grouping of spatially connected image pixels with similar
feature representations, we introduce a spatial consistency loss term. This
aids the network in effectively capturing the relationships among neighboring
pixels and enhancing the overall segmentation quality. The S$^3$-Net approach
iteratively learns pixel-level feature representations for image content
clustering in an end-to-end manner. Our experimental results on skin lesion and
lung organ segmentation tasks show the superior performance of our method
compared to the SOTA approaches. this https URL

通过使用自监督算法 S³-Net 结合 Inception Large Kernel Attention（I-LKA）模块、可变形卷积和空间一致性损失项，本研究提出一种精确医学图像分割方法，并在皮肤病变和肺器官分割任务中展现了超越 SOTA 方法的卓越性能。

自我监督语义分割：变换一致性

Self-supervised Semantic Segmentation: Consistency over Transformation

Fact-checking has gained increasing attention due to the widespread of
falsified information. Most fact-checking approaches focus on claims made in
English only due to the data scarcity issue in other languages. The lack of
fact-checking datasets in low-resource languages calls for an effective
cross-lingual transfer technique for fact-checking. Additionally, trustworthy
information in different languages can be complementary and helpful in
verifying facts. To this end, we present the first fact-checking framework
augmented with cross-lingual retrieval that aggregates evidence retrieved from
multiple languages through a cross-lingual retriever. Given the absence of
cross-lingual information retrieval datasets with claim-like queries, we train
the retriever with our proposed Cross-lingual Inverse Cloze Task (X-ICT), a
self-supervised algorithm that creates training instances by translating the
title of a passage. The goal for X-ICT is to learn cross-lingual retrieval in
which the model learns to identify the passage corresponding to a given
translated title. On the X-Fact dataset, our approach achieves 2.23% absolute
F1 improvement in the zero-shot cross-lingual setup over prior systems. The
source code and data are publicly available at
this https URL.

为了解决低资源语言事实核查数据集的缺乏问题，我们提出了一种跨语言检索的事实核查框架，该框架利用我们提出的自监督算法，使用翻译的文章标题来创建训练实例，并在多种语言下实现证据的汇集。在 X-Fact 数据集上，我们的方法在零 - shot 跨语言设置下，在绝对 F1 上获得了 2.23% 的改进。