Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. We project that the gap between the number of imaging exams and the number of expert radiologist readers required to cover this increase will continue to expand, consequently introducing a demand for AI-based tools that improve the efficiency with which radiologists can comfortably interpret these exams. AI has been shown to improve efficiency in medical-image generation, processing, and interpretation, and a variety of such AI models have been developed across research labs worldwide. However, very few of these, if any, find their way into routine clinical use, a discrepancy that reflects the divide between AI research and successful AI translation. To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation. This report represents several years of weekly discussions and hands-on problem solving experience by groups of industry experts and clinicians in the MONAI Consortium. We identify barriers between AI-model development in research labs and subsequent clinical deployment and propose solutions. Our report provides guidance on processes which take an imaging AI model from development to clinical implementation in a healthcare institution. We discuss various AI integration points in a clinical Radiology workflow. We also present a taxonomy of Radiology AI use-cases. Through this report, we intend to educate the stakeholders in healthcare and AI (AI researchers, radiologists, imaging informaticists, and regulators) about cross-disciplinary challenges and possible solutions.

Corals are the primary habitat-building life-form on reefs that support a quarter of the species in the ocean. A coral reef ecosystem usually consists of reefs, each of which is like a tall building in any city. These reef-building corals secrete hard calcareous exoskeletons that give them structural rigidity, and are also a prerequisite for our accurate 3D modeling and semantic mapping using advanced photogrammetric computer vision and machine learning. Underwater videography as a modern underwater remote sensing tool is a high-resolution coral habitat survey and mapping technique. In this paper, detailed 3D mesh models, digital surface models and orthophotos of the coral habitat are generated from the collected coral images and underwater control points. Meanwhile, a novel pixel-wise semantic segmentation approach of orthophotos is performed by advanced deep learning. Finally, the semantic map is mapped into 3D space. For the first time, 3D fine-grained semantic modeling and rugosity evaluation of coral reefs have been completed at millimeter (mm) accuracy. This provides a new and powerful method for understanding the processes and characteristics of coral reef change at high spatial and temporal resolution under climate change.

This paper describes the submission of the RoyalFlush neural machine translation system for the WMT 2022 translation efficiency task. Unlike the commonly used autoregressive translation system, we adopted a two-stage translation paradigm called Hybrid Regression Translation (HRT) to combine the advantages of autoregressive and non-autoregressive translation. Specifically, HRT first autoregressively generates a discontinuous sequence (e.g., make a prediction every $k$ tokens, $k>1$) and then fills in all previously skipped tokens at once in a non-autoregressive manner. Thus, we can easily trade off the translation quality and speed by adjusting $k$. In addition, by integrating other modeling techniques (e.g., sequence-level knowledge distillation and deep-encoder-shallow-decoder layer allocation strategy) and a mass of engineering efforts, HRT improves 80\% inference speed and achieves equivalent translation performance with the same-capacity AT counterpart. Our fastest system reaches 6k+ words/second on the GPU latency setting, estimated to be about 3.1x faster than the last year's winner.

This paper presents BigCilin, the first Chinese open-domain knowledge graph with fine-grained hypernym-hyponym re-lations which are extracted automatically from multiple sources for Chinese named entities. With the fine-grained hypernym-hyponym relations, BigCilin owns flexible semantic hierarchical structure. Since the hypernym-hyponym paths are automati-cally generated and one entity may have several senses, we provide a path disambi-guation solution to map a hypernym-hyponym path of one entity to its one sense on the condition that the path and the sense express the same meaning. In order to conveniently access our BigCilin Knowle-dge graph, we provide web interface in two ways. One is that it supports querying any Chinese named entity and browsing the extracted hypernym-hyponym paths surro-unding the query entity. The other is that it gives a top-down browsing view to illust-rate the overall hierarchical structure of our BigCilin knowledge graph over some sam-pled entities.

用于提取和抽象性摘要系统的传统培训范例始终仅使用令牌级别或句子级培训目标。但是，始终从摘要级别评估输出摘要，从而导致培训和评估的不一致。在本文中，我们提出了一个基于对比度学习的重新排列框架，用于一阶段的摘要，称为COLO。通过建模对比目标，我们表明摘要模型能够根据摘要级别的分数直接生成摘要，而无需其他模块和参数。广泛的实验表明，CORO在CNN/DailyMail基准测试中提高了单阶段系统的提取和抽象结果，将其提高到44.58和46.33 Rouge-1得分，同时保留了参数效率和推断效率。与最先进的多阶段系统相比，我们节省了100多个GPU训练时间，并在推理期间获得3〜8加速比，同时保持可比的结果。

本文回顾了AIM 2022上压缩图像和视频超级分辨率的挑战。这项挑战包括两条曲目。轨道1的目标是压缩图像的超分辨率，轨迹〜2靶向压缩视频的超分辨率。在轨道1中，我们使用流行的数据集DIV2K作为培训，验证和测试集。在轨道2中，我们提出了LDV 3.0数据集，其中包含365个视频，包括LDV 2.0数据集（335个视频）和30个其他视频。在这一挑战中，有12支球队和2支球队分别提交了赛道1和赛道2的最终结果。所提出的方法和解决方案衡量了压缩图像和视频上超分辨率的最先进。提出的LDV 3.0数据集可在https://github.com/renyang-home/ldv_dataset上找到。此挑战的首页是在https://github.com/renyang-home/aim22_compresssr。

尽管在半监督语义细分领域的进度程度不同，但其最近的大部分成功都涉及笨拙的模型，并且尚未探索轻量级解决方案。我们发现，现有的知识蒸馏技术更多地关注标签数据中的像素级概念，该数据未能在未标记的数据中考虑更有用的线索。因此，我们提供了首次尝试通过新颖的多晶蒸馏（MGD）方案提供轻量级SSS模型，其中从三个方面捕获了多个跨性别：i）互补的教师结构； ii）标记为未标记的数据合作蒸馏； iii）分层和多层次损失设置。具体而言，MGD被配制为标记的未标记数据合作蒸馏方案，该方案有助于充分利用在半监督环境中必不可少的不同数据特征。图像水平的语义敏感损失，区域级别的内容感知损失和像素级的一致性损失是通过结构互补的教师来丰富层次蒸馏抽象的。 Pascal VOC2012和CityScapes的实验结果表明，在不同的分区协议下，MGD可以超越竞争方法。例如，在1/16的CityScapes分区协议下，RESNET-18和MOBILENET-V2主链的性能分别增长了11.5％和4.6％。尽管模型骨干的拖曳量被3.4-5.3倍（RESNET-18）和38.7-59.6X（MobileNetV2）压缩，但该模型旨在实现令人满意的分割结果。

近年来，随着新颖的策略和应用，神经网络一直在迅速扩展。然而，尽管不可避免地会针对关键应用程序来解决这些挑战，例如神经网络技术诸如神经网络技术中仍未解决诸如神经网络技术的挑战。已经尝试通过用符号表示来表示和嵌入域知识来克服神经网络计算中的挑战。因此，出现了神经符号学习（Nesyl）概念，其中结合了符号表示的各个方面，并将常识带入神经网络（Nesyl）。在可解释性，推理和解释性至关重要的领域中，例如视频和图像字幕，提问和推理，健康信息学和基因组学，Nesyl表现出了有希望的结果。这篇综述介绍了一项有关最先进的Nesyl方法的全面调查，其原理，机器和深度学习算法的进步，诸如Opthalmology之类的应用以及最重要的是该新兴领域的未来观点。

知识图完成最近已广泛研究，以通过主要建模图结构特征来完成三元组中的缺失元素，但对图形结构的稀疏性敏感。期望解决这一挑战的相关文本，例如实体名称和描述，充当知识图（kgs）的另一种表达形式（kgs）。已经提出了几种使用两个编码器的结构和文本消息的方法，但由于未能平衡它们之间的权重有限。并在推理期间保留结构和文本编码器，也遭受了沉重的参数。通过知识蒸馏的激励，我们将知识视为从输入到输出概率的映射，并在稀疏的kgs上提出了一个插件框架VEM2L，以将从文本和结构消息提取到统一的知识中融合知识。具体而言，我们将模型获取的知识分配为两个不重叠的部分：一个部分与训练三元组合的合适能力有关，可以通过激励两个编码者互相学习训练集来融合。另一个反映了未观察到的查询的概括能力。相应地，我们提出了一种新的融合策略，该策略由变量EM算法证明，以融合模型的概括能力，在此期间，我们还应用图形致密操作以进一步缓解稀疏的图形问题。通过结合这两种融合方法，我们最终提出了VEM2L框架。详细的理论证据以及定量和定性实验都证明了我们提出的框架的有效性和效率。

最近，基于合成数据的实例分割已成为一种极其有利的优化范式，因为它利用模拟渲染和物理学来生成高质量的图像宣传对。在本文中，我们提出了一个并行预训练的变压器（PPT）框架，以完成基于合成数据的实例分割任务。具体而言，我们利用现成的预训练的视觉变压器来减轻自然数据和合成数据之间的差距，这有助于在下游合成数据场景中提供良好的概括，几乎没有样本。基于SWIN-B基的CBNET V2，基于SWINL的CBNET V2和SWIN-L基统一器用于并行特征学习，并且这三个模型的结果由像素级非最大最大抑制（NMS）算法融合来获得更强大的结果。实验结果表明，PPT在CVPR2022 AVA可访问性视觉和自主性挑战中排名第一，地图为65.155％。