当测试图像提出看不见的分布时，深层分割模型通常会面临故障风险。改善模型鲁棒性针对这些风险的鲁棒性对于深层模型的大规模临床应用至关重要。在这项研究中，受到人类学习周期的启发，我们提出了一个新颖的在线反思学习框架（REFSEG），以改善细分鲁棒性。基于启用概念的反射概念，我们的refseg首先驱动了深层模型以采取行动以获得语义分割。然后，refseg触发模型以反映自身。因为使深层模型在测试过程中意识到他们的细分失败是具有挑战性的，所以RefSeg合成了从语义面具中综合的逼真的代理图像，以帮助深层模型构建直观有效的反射。该代理翻译并强调了分割缺陷。通过最大程度地提高原始输入和代理之间的结构相似性，可以改善分割鲁棒性的反射循环。 REFSEG在测试阶段运行，并且是分割模型的一般性。通过公共心脏MR数据集和两个内部大型超声数据集对三个医疗图像细分任务进行了广泛的验证，这表明我们的refseg显着提高了模型的鲁棒性，并报告了与强大竞争对手有关的最先进的表现。

标准平面（SP）定位对于常规临床超声（US）诊断至关重要。与2D US相比，3D US可以一次扫描获得多个视图平面，并通过添加冠状平面提供完整的解剖结构。但是，由于方向的可变性和巨大的搜索空间，在3D US中手动导航SPS是费力的和有偏见的。在这项研究中，我们介绍了3D US中自动SP本地化的新型增强学习（RL）框架。我们的贡献是三倍。首先，我们将3D中的SP定位作为RL中的基于切线的问题，以重组动作空间并大大降低搜索空间。其次，我们设计了一种辅助任务学习策略，以增强模型识别跨越平面搜索中非SPS和SP的微妙差异的能力。最后，我们通过同时利用空间和解剖学信息来提出空间 - 动态奖励，以有效地指导学习轨迹。我们探讨了我们方法在子宫和胎儿脑数据集上定位四个SP的功效。实验表明，我们的方法达到了较高的定位精度以及稳健的性能。

复发性喉神经（RLN）的肿瘤浸润是机器人甲状腺切除术的禁忌症，很难通过标准喉镜检测。超声（US）是RLN检测的可行替代方法，因为其安全性和提供实时反馈的能力。但是，直径通常小于3mm的RLN的微小性对RLN的准确定位构成了重大挑战。在这项工作中，我们为RLN本地化提出了一个知识驱动的框架，模仿了外科医生根据其周围器官识别RLN的标准方法。我们基于器官之间固有的相对空间关系构建了先前的解剖模型。通过贝叶斯形状比对（BSA），我们获得了围绕RLN的感兴趣区域（ROI）中心的候选坐标。 ROI允许使用基于多尺度语义信息的双路径识别网络确定RLN的精制质心的视野减少。实验结果表明，与最先进的方法相比，所提出的方法达到了较高的命中率和距离较小的距离误差。

深度神经网络（DNN）在解决各种领域的不同任务方面取得了非凡的性能。然而，传统的DNN模型通过损耗反向化稳定地接近地面真值。在某些应用中，可以容易地获得一些先验的知识，例如在遵循地面真理观察的约束。在这里，我们尝试提供一种普遍的方法来从这些约束中纳入信息以增强DNN的性能。从理论上讲，我们可以将这些类型的问题制定为KKT条件可以解决的受限优化问题。在本文中，我们建议在DNN中使用可分化的投影层，而不是直接求解耗时的KKT条件。所提出的投影方法可分辨，并且不需要重大计算。最后，我们还使用Pascal VOC DataSet使用随机生成的合成数据集和图像分割任务进行了一些实验，以评估所提出的投影方法的性能。实验结果表明，投影方法足够且优于基线方法。

由于变形领域缺乏原因，可变形的登记模型广泛采用无监督的学习策略。这些模型通常取决于基于强度的相似性损失，以获得学习融合。尽管成功，但这种依赖性不足。对于单片机图像的可变形登记，对准的两个图像不仅具有难以区分的强度差异，而且在统计分布和边界区域中也紧密。考虑到设计良好的损耗功能可以促进学习模型进入所需的收敛，我们通过通过混合丢失集成多个图像特性来学习T1加权MR图像的可变形登记模型。我们的方法在保持变形平滑度的同时以高精度注册OASIS数据集。

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

Surgical robot automation has attracted increasing research interest over the past decade, expecting its huge potential to benefit surgeons, nurses and patients. Recently, the learning paradigm of embodied AI has demonstrated promising ability to learn good control policies for various complex tasks, where embodied AI simulators play an essential role to facilitate relevant researchers. However, existing open-sourced simulators for surgical robot are still not sufficiently supporting human interactions through physical input devices, which further limits effective investigations on how human demonstrations would affect policy learning. In this paper, we study human-in-the-loop embodied intelligence with a new interactive simulation platform for surgical robot learning. Specifically, we establish our platform based on our previously released SurRoL simulator with several new features co-developed to allow high-quality human interaction via an input device. With these, we further propose to collect human demonstrations and imitate the action patterns to achieve more effective policy learning. We showcase the improvement of our simulation environment with the designed new features and tasks, and validate state-of-the-art reinforcement learning algorithms using the interactive environment. Promising results are obtained, with which we hope to pave the way for future research on surgical embodied intelligence. Our platform is released and will be continuously updated in the website: https://med-air.github.io/SurRoL/

Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage. Existing computational methods on MLS quantification not only require intensive labeling in millimeter-level measurement but also suffer from poor performance due to their dependence on specific landmarks or simplified anatomical assumptions. In this paper, we propose a novel semi-supervised framework to accurately measure the scale of MLS from head CT scans. We formulate the MLS measurement task as a deformation estimation problem and solve it using a few MLS slices with sparse labels. Meanwhile, with the help of diffusion models, we are able to use a great number of unlabeled MLS data and 2793 non-MLS cases for representation learning and regularization. The extracted representation reflects how the image is different from a non-MLS image and regularization serves an important role in the sparse-to-dense refinement of the deformation field. Our experiment on a real clinical brain hemorrhage dataset has achieved state-of-the-art performance and can generate interpretable deformation fields.

Text-to-SQL semantic parsing is an important NLP task, which greatly facilitates the interaction between users and the database and becomes the key component in many human-computer interaction systems. Much recent progress in text-to-SQL has been driven by large-scale datasets, but most of them are centered on English. In this work, we present MultiSpider, the largest multilingual text-to-SQL dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnamese). Upon MultiSpider, we further identify the lexical and structural challenges of text-to-SQL (caused by specific language properties and dialect sayings) and their intensity across different languages. Experimental results under three typical settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. Qualitative and quantitative analyses are conducted to understand the reason for the performance drop of each language. Besides the dataset, we also propose a simple schema augmentation framework SAVe (Schema-Augmentation-with-Verification), which significantly boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages.

Table-and-text hybrid question answering (HybridQA) is a widely used and challenging NLP task commonly applied in the financial and scientific domain. The early research focuses on migrating other QA task methods to HybridQA, while with further research, more and more HybridQA-specific methods have been present. With the rapid development of HybridQA, the systematic survey is still under-explored to summarize the main techniques and advance further research. So we present this work to summarize the current HybridQA benchmarks and methods, then analyze the challenges and future directions of this task. The contributions of this paper can be summarized in three folds: (1) first survey, to our best knowledge, including benchmarks, methods and challenges for HybridQA; (2) systematic investigation with the reasonable comparison of the existing systems to articulate their advantages and shortcomings; (3) detailed analysis of challenges in four important dimensions to shed light on future directions.