An increasing number of public datasets have shown a marked clinical impact on assessing anatomical structures. However, each of the datasets is small, partially labeled, and rarely investigates severe tumor subjects. Moreover, current models are limited to segmenting specific organs/tumors, which can not be extended to novel domains and classes. To tackle these limitations, we introduce embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models, dubbed the CLIP-Driven Universal Model. The Universal Model can better segment 25 organs and 6 types of tumors by exploiting the semantic relationship between abdominal structures. The model is developed from an assembly of 14 datasets with 3,410 CT scans and evaluated on 6,162 external CT scans from 3 datasets. We rank first on the public leaderboard of the Medical Segmentation Decathlon (MSD) and achieve the state-of-the-art results on Beyond The Cranial Vault (BTCV). Compared with dataset-specific models, the Universal Model is computationally more efficient (6x faster), generalizes better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks. The design of CLIP embedding enables the Universal Model to be easily extended to new classes without catastrophically forgetting the previously learned classes.

Deep learning-based 3D object detectors have made significant progress in recent years and have been deployed in a wide range of applications. It is crucial to understand the robustness of detectors against adversarial attacks when employing detectors in security-critical applications. In this paper, we make the first attempt to conduct a thorough evaluation and analysis of the robustness of 3D detectors under adversarial attacks. Specifically, we first extend three kinds of adversarial attacks to the 3D object detection task to benchmark the robustness of state-of-the-art 3D object detectors against attacks on KITTI and Waymo datasets, subsequently followed by the analysis of the relationship between robustness and properties of detectors. Then, we explore the transferability of cross-model, cross-task, and cross-data attacks. We finally conduct comprehensive experiments of defense for 3D detectors, demonstrating that simple transformations like flipping are of little help in improving robustness when the strategy of transformation imposed on input point cloud data is exposed to attackers. Our findings will facilitate investigations in understanding and defending the adversarial attacks against 3D object detectors to advance this field.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

卷积神经网络（CNN）通过深度体系结构获得了出色的性能。但是，这些CNN在复杂的场景下通常对图像超分辨率（SR）实现较差的鲁棒性。在本文中，我们通过利用不同类型的结构信息来获得高质量图像，提出了异质组SR CNN（HGSRCNN）。具体而言，HGSRCNN的每个异质组块（HGB）都采用含有对称组卷积块和互补的卷积块的异质体系结构，并以平行方式增强不同渠道的内部和外部关系，以促进富裕类型的较富裕类型的信息， 。为了防止出现获得的冗余功能，以串行方式具有信号增强功能的完善块旨在过滤无用的信息。为了防止原始信息的丢失，多级增强机制指导CNN获得对称架构，以促进HGSRCNN的表达能力。此外，开发了一种平行的向上采样机制来训练盲目的SR模型。广泛的实验表明，在定量和定性分析方面，提出的HGSRCNN获得了出色的SR性能。可以在https://github.com/hellloxiaotian/hgsrcnn上访问代码。

深度学习取得了长足的进步，用于图像中的对象检测。对象检测的检测准确性和计算成本取决于图像的空间分辨率，这可能会受到相机和存储注意事项的约束。压缩通常是通过减少空间或幅度分辨率或有时两者都对性能的众所周知的影响来实现的。检测精度还取决于感兴趣的对象与摄像机的距离。我们的工作研究了空间和振幅分辨率以及对象距离对物体检测准确性和计算成本的影响。我们开发了Yolov5（ra-Yolo）的分辨率 - 自适应变体，该变体基于输入图像的空间分辨率，它在特征金字塔和检测头中变化。为了训练和评估这种新方法，我们通过结合TJU和Eurocity数据集的图像来创建具有不同空间和振幅分辨率的图像数据集，并通过应用空间调整和压缩来生成不同的分辨率。我们首先表明Ra-Yolo在各种空间分辨率上实现了检测准确性和推理时间之间的良好权衡。然后，我们使用拟议的RA-YOLO模型评估空间和振幅分辨率对物体检测准确性的影响。我们证明，导致最高检测精度的最佳空间分辨率取决于“耐受性”图像大小。我们进一步评估了对象到摄像机对检测准确性的影响，并表明较高的空间分辨率可实现更大的检测范围。这些结果为选择图像空间分辨率和压缩设置提供了重要的指南，这些分辨率和压缩设置基于可用的带宽，存储，所需的推理时间和/或所需的检测范围，在实际应用中。

由遮挡，信号丢失或手动注释错误引起的3D边界框的地面真相注释的固有歧义可能会使训练过程中的深3D对象检测器混淆，从而使检测准确性恶化。但是，现有方法在某种程度上忽略了此类问题，并将标签视为确定性。在本文中，我们提出了GLENET，这是一个从条件变异自动编码器改编的生成标签不确定性估计框架，以建模典型的3D对象与其潜在的潜在基边界框之间具有潜在变量的一对一关系。 Glenet产生的标签不确定性是一个插件模块，可以方便地集成到现有的深3D检测器中，以构建概率检测器并监督本地化不确定性的学习。此外，我们提出了概率探测器中的不确定性质量估计量架构，以指导对IOU分支的培训，并预测了本地化不确定性。我们将提出的方法纳入各种流行的3D检测器中，并观察到它们的性能显着提高到Waymo Open DataSet和Kitti数据集中的当前最新技术。

以有限的注释成本收集的嘈杂标签可阻止医疗图像分割算法学习精确的语义相关性。先前使用嘈杂标签的学习的细分艺术仅执行以像素的方式来保留语义，例如像素标签校正，但忽略了配对的方式。实际上，我们观察到，捕获像素之间亲和力关系的成对方式可以大大降低标签噪声率。在这一观察结果的推动下，我们通过纳入像素和配对的方式来介绍了缓解嘈杂的新观点，分别从嘈杂的阶级和亲和力标签中得出了监督。统一像素和配对的举止，我们提出了一个强大的联合类亲和力分割（JCAS）框架，以解决医疗图像分割中的标签噪声问题。考虑到成对方式的亲和力结合了上下文依赖性，通过推理有关类内部和类的亲和力关系来设计区分的亲和力推理（DAR）模块来纠正像素段预测。为了进一步增强噪声阻力，旨在通过类和亲和力标签中建模的噪声标签分布来纠正监督信号的类亲和力损失校正（计算）策略。同时，CALC策略通过理论得出的一致性正则化来互动像素和成对的方式。合成和现实世界噪声标签下的广泛实验证实了所提出的JCAS框架的功效，并且对上限性能的最小间隙。源代码可在\ url {https://github.com/cityu-aim-group/jcas}中获得。

具有强大学习能力的CNN被广泛选择以解决超分辨率问题。但是，CNN依靠更深的网络体系结构来提高图像超分辨率的性能，这可能会增加计算成本。在本文中，我们提出了一个增强的超分辨率组CNN（ESRGCNN），具有浅层架构，通过完全融合了深层和宽的通道特征，以在单图超级分辨率中的不同通道的相关性提取更准确的低频信息（ SISR）。同样，ESRGCNN中的信号增强操作对于继承更长途上下文信息以解决长期依赖性也很有用。将自适应上采样操作收集到CNN中，以获得具有不同大小的低分辨率图像的图像超分辨率模型。广泛的实验报告说，我们的ESRGCNN在SISR中的SISR性能，复杂性，执行速度，图像质量评估和SISR的视觉效果方面超过了最先进的实验。代码可在https://github.com/hellloxiaotian/esrgcnn上找到。

先进的可穿戴设备越来越多地利用高分辨率多摄像头系统。作为用于处理所得到的图像数据的最先进的神经网络是计算要求的，对于利用第五代（5G）无线连接和移动边缘计算，已经越来越感兴趣，以将该处理卸载到云。为了评估这种可能性，本文提出了一个详细的仿真和评估，用于5G无线卸载，用于对象检测，在一个名为Vis4ion的强大新型智能可穿戴物中，用于盲目损害（BVI）。目前的Vis4ion系统是一种具有高分辨率摄像机，视觉处理和触觉和音频反馈的仪表簿。本文认为将相机数据上载到移动边缘云以执行实时对象检测并将检测结果传输回可穿戴。为了确定视频要求，纸张评估视频比特率和分辨率对物体检测精度和范围的影响。利用与BVI导航相关的标记对象的新街道场景数据集进行分析。视觉评估与详细的全堆栈无线网络仿真结合，以确定吞吐量的分布和延迟，具有来自城市环境中的新高分辨率3D模型的实际导航路径和射线跟踪。为了比较，无线仿真考虑了标准的4G长期演进（LTE）载波和高速度5G毫米波（MMWAVE）载波。因此，该工作提供了对具有高带宽和低延迟要求的应用中的MMWAVE连接的边缘计算的彻底和现实评估。

由于成像装置的约束和操作时间的高成本，电脑断层扫描（CT）扫描通常以低帧内分辨率获取。改善切片内分辨率对人类专家和计算机辅助系统的疾病诊断有益。为此，本文建立了一种新型医疗切片合成，以增加切片分辨率。考虑到临床实践中始终缺乏地面真理中间医学切片，我们介绍了以自我监督的学习方式实现这项任务的增量跨视图相互蒸馏策略。具体而言，我们从三种不同的视图模型在这种情况下，从不同视图中学到的模型可以蒸馏有价值的知识来引导彼此的学习过程。我们可以重复此过程以使模型通过增加切片分辨率来综合中间切片数据。为了证明所提出的方法的有效性，我们对大型CT数据集进行了全面的实验。定量和定性比较结果表明，我们的方法通过清晰的边缘来占据最先进的算法。