Successful identification of blood vessel blockage is a crucial step for Alzheimer's disease diagnosis. These blocks can be identified from the spatial and time-depth variable Two-Photon Excitation Microscopy (TPEF) images of the brain blood vessels using machine learning methods. In this study, we propose several preprocessing schemes to improve the performance of these methods. Our method includes 3D-point cloud data extraction from image modality and their feature-space fusion to leverage complementary information inherent in different modalities. We also enforce the learned representation to be sequence-order invariant by utilizing bi-direction dataflow. Experimental results on The Clog Loss dataset show that our proposed method consistently outperforms the state-of-the-art preprocessing methods in stalled and non-stalled vessel classification.

最近，融合了激光雷达点云和相机图像，提高了3D对象检测的性能和稳健性，因为这两种方式自然具有强烈的互补性。在本文中，我们通过引入新型级联双向融合〜（CB融合）模块和多模态一致性〜（MC）损耗来提出用于多模态3D对象检测的EPNet ++。更具体地说，所提出的CB融合模块提高点特征的丰富语义信息，以级联双向交互融合方式具有图像特征，导致更全面且辨别的特征表示。 MC损失明确保证预测分数之间的一致性，以获得更全面且可靠的置信度分数。基蒂，JRDB和Sun-RGBD数据集的实验结果展示了通过最先进的方法的EPNet ++的优越性。此外，我们强调一个关键但很容易被忽视的问题，这是探讨稀疏场景中的3D探测器的性能和鲁棒性。广泛的实验存在，EPNet ++优于现有的SOTA方法，在高稀疏点云壳中具有显着的边距，这可能是降低LIDAR传感器的昂贵成本的可用方向。代码将来会发布。

Lung cancer is a severe menace to human health, due to which millions of people die because of late diagnoses of cancer; thus, it is vital to detect the disease as early as possible. The Computerized chest analysis Tomography of scan is assumed to be one of the efficient solutions for detecting and classifying lung nodules. The necessity of high accuracy of analyzing C.T. scan images of the lung is considered as one of the crucial challenges in detecting and classifying lung cancer. A new long-short-term-memory (LSTM) based deep fusion structure, is introduced, where, the texture features computed from lung nodules through new volumetric grey-level-co-occurrence-matrices (GLCM) computations are applied to classify the nodules into: benign, malignant and ambiguous. An improved Otsu segmentation method combined with the water strider optimization algorithm (WSA) is proposed to detect the lung nodules. Otsu-WSA thresholding can overcome the restrictions present in previous thresholding methods. Extended experiments are run to assess this fusion structure by considering 2D-GLCM computations based 2D-slices fusion, and an approximation of this 3D-GLCM with volumetric 2.5D-GLCM computations-based LSTM fusion structure. The proposed methods are trained and assessed through the LIDC-IDRI dataset, where 94.4%, 91.6%, and 95.8% Accuracy, sensitivity, and specificity are obtained, respectively for 2D-GLCM fusion and 97.33%, 96%, and 98%, accuracy, sensitivity, and specificity, respectively, for 2.5D-GLCM fusion. The yield of the same are 98.7%, 98%, and 99%, for the 3D-GLCM fusion. The obtained results and analysis indicate that the WSA-Otsu method requires less execution time and yields a more accurate thresholding process. It is found that 3D-GLCM based LSTM outperforms its counterparts.

Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both two modalities of images have prominent biomarkers to indicate glaucoma suspected. Clinically, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment. Inspired by the success of Retinal Fundus Glaucoma Challenge (REFUGE) we held previously, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus \& OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, top-10 teams were selected to the final stage. We analysis their results and summarize their methods in the paper. Since all these teams submitted their source code in the challenge, a detailed ablation study is also conducted to verify the effectiveness of the particular modules proposed. We find many of the proposed techniques are practical for the clinical diagnosis of glaucoma. As the first in-depth study of fundus \& OCT multi-modality glaucoma grading, we believe the GAMMA Challenge will be an essential starting point for future research.

婴儿对一般运动（GM）的评估是早期诊断神经发育障碍的有用工具。但是，其在临床实践中的评估依赖于专家的视觉检查，并且热切期待自动解决方案。最近，基于视频的GMS分类引起了人们的注意，但是这种方法将受到无关信息的强烈影响，例如视频中的背景混乱。此外，为了可靠性，有必要在GMS期间正确提取婴儿的时空特征。在这项研究中，我们提出了一种自动GMS分类方法，该方法由预处理网络组成，该网络从GMS视频中删除不必要的背景信息并调整婴儿的身体位置以及基于两流结构的后续运动分类网络。提出的方法可以有效地提取GMS分类的基本时空特征，同时防止过度拟合与不同记录环境无关的信息。我们使用从100名婴儿获得的视频验证了提出的方法。实验结果表明，所提出的方法的表现优于几个基线模型和现有方法。

Human activity recognition (HAR) using drone-mounted cameras has attracted considerable interest from the computer vision research community in recent years. A robust and efficient HAR system has a pivotal role in fields like video surveillance, crowd behavior analysis, sports analysis, and human-computer interaction. What makes it challenging are the complex poses, understanding different viewpoints, and the environmental scenarios where the action is taking place. To address such complexities, in this paper, we propose a novel Sparse Weighted Temporal Attention (SWTA) module to utilize sparsely sampled video frames for obtaining global weighted temporal attention. The proposed SWTA is comprised of two parts. First, temporal segment network that sparsely samples a given set of frames. Second, weighted temporal attention, which incorporates a fusion of attention maps derived from optical flow, with raw RGB images. This is followed by a basenet network, which comprises a convolutional neural network (CNN) module along with fully connected layers that provide us with activity recognition. The SWTA network can be used as a plug-in module to the existing deep CNN architectures, for optimizing them to learn temporal information by eliminating the need for a separate temporal stream. It has been evaluated on three publicly available benchmark datasets, namely Okutama, MOD20, and Drone-Action. The proposed model has received an accuracy of 72.76%, 92.56%, and 78.86% on the respective datasets thereby surpassing the previous state-of-the-art performances by a margin of 25.26%, 18.56%, and 2.94%, respectively.

冠心病（CHD）是现代世界中死亡的主要原因。用于诊断和治疗CHD的现代分析工具的开发正在从科学界受到极大的关注。基于深度学习的算法，例如分割网络和检测器，通过及时分析患者的血管造影来协助医疗专业人员，在协助医疗专业人员方面发挥着重要作用。本文着重于X射线冠状动脉造影（XCA），该血管造影被认为是CHD诊断和治疗中的“黄金标准”。首先，我们描述了XCA图像的公开可用数据集。然后，审查了图像预处理的经典和现代技术。此外，讨论了共同的框架选择技术，这是输入质量以及模型性能的重要因素。在以下两章中，我们讨论了现代血管分割和狭窄检测网络，最后是当前最新技术的开放问题和当前局限性。

我们提出了跨模式的细心连接，这是一种从可穿戴数据中学习的新型动态和有效技术。我们的解决方案可以集成到管道的任何阶段，即在任何卷积层或块之后，以在负责处理每种模式的单个流之间创建中间连接。此外，我们的方法受益于两个属性。首先，它可以单向共享信息（从一种方式到另一种方式）或双向分别。其次，可以同时将其集成到多个阶段中，以进一步允许以几个接触点交换网络梯度。我们对三个公共多模式可穿戴数据集（Wesad，Swell-KW和案例）进行了广泛的实验，并证明我们的方法可以有效地调节不同模式之间的信息，以学习更好的表示。我们的实验进一步表明，一旦整合到基于CNN的多模式溶液（2、3或4模态）中，我们的方法就会导致卓越或竞争性的性能，而不是最先进的表现，并且表现优于各种基线模式和经典的多模式方法。

多模态数据在遥感（RS）中变得容易获得，并且可以提供有关地球表面的互补信息。因此，多模态信息的有效融合对于卢比的各种应用是重要的，而且由于域差异，噪音和冗余，也是非常具有挑战性的。缺乏有效和可扩展的融合技术，用于遍布多种模式编码器和完全利用互补信息。为此，我们提出了一种基于新型金字塔注意融合（PAF）模块和门控融合单元（GFU）的多模态遥感数据的新型多模态网络（Multimodnet）。 PAF模块旨在有效地从每个模态中获得丰富的细粒度上下文表示，具有内置的交叉级别和巧克力关注融合机制，GFU模块利用了新颖的门控机制，用于早期合并特征，从而降低隐藏的冗余和噪音。这使得可以有效地提取补充方式来提取最迟到的特征融合的最有价值和互补的信息。两个代表性RS基准数据集的广泛实验证明了多模态土地覆盖分类的多模型的有效性，鲁棒性和优越性。

由直觉的激励，即在相应的3D点云中定位2D图像的关键步骤正在建立它们之间的2d-3d对应关系，我们提出了第一个基于特征的密度通信框架，以解决图像到点云注册问题，称为Corri2p，由三个模块组成，即特征嵌入，对称重叠区域检测和通过已建立的对应关系构成估计。具体而言，给定一对2D图像和3D点云，我们首先将它们转换为高维特征空间，并将结果特征馈入对称重叠区域检测器，以确定图像和点云相互重叠的区域。然后，我们使用重叠区域的功能在RANSAC内运行EPNP之前以估算相机的姿势，以建立2D-3D对应关系。 Kitti和Nuscenes数据集的实验结果表明，我们的Corri2p优于最先进的图像到点云注册方法。我们将公开提供代码。

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 3rd International Workshop on Reading Music Systems, held in Alicante on the 23rd of July 2021.

计算机视觉任务可以从估计突出物区域和这些对象区域之间的相互作用中受益。识别对象区域涉及利用预借鉴模型来执行对象检测，对象分割和/或对象姿势估计。但是，由于以下原因，在实践中不可行：1）预用模型的训练数据集的对象类别可能不会涵盖一般计算机视觉任务的所有对象类别，2）佩戴型模型训练数据集之间的域间隙并且目标任务的数据集可能会影响性能，3）预磨模模型中存在的偏差和方差可能泄漏到导致无意中偏置的目标模型的目标任务中。为了克服这些缺点，我们建议利用一系列视频帧捕获一组公共对象和它们之间的相互作用的公共基本原理，因此视频帧特征之间的共分割的概念可以用自动的能力装配模型专注于突出区域，以最终的方式提高潜在的任务的性能。在这方面，我们提出了一种称为“共分割激活模块”（COSAM）的通用模块，其可以被插入任何CNN，以促进基于CNN的任何CNN的概念在一系列视频帧特征中的关注。我们在三个基于视频的任务中展示Cosam的应用即1）基于视频的人Re-ID，2）视频字幕分类，并证明COSAM能够在视频帧中捕获突出区域，从而引导对于显着的性能改进以及可解释的关注图。

扩散MRI拖拉术是一种先进的成像技术，可实现大脑白质连接的体内映射。白质拟层将拖拉机分类为簇或解剖学上有意义的区域。它可以量化和可视化全脑拖拉学。当前，大多数拟层方法都集中在深白质（DWM）上，而由于其复杂性，更少的方法解决了浅表白质（SWM）。我们提出了一种新型的两阶段深度学习的框架，即浅表白质分析（SUPWMA​​），该框架对全脑拖拉机的198个SWM簇进行了有效且一致的分析。一个基于点云的网络适应了我们的SWM分析任务，并且监督的对比度学习可以在SWM的合理流线和离群值之间进行更多的歧视性表示。我们在大规模拖拉机数据集上训练模型，包括来自标签的SWM簇和解剖学上难以置信的流线样本的简化样品，我们对六个不同年龄和健康状况的独立获取的数据集进行测试（包括新生儿和具有空间型脑肿瘤的患者） ）。与几种最先进的方法相比，SupWMA在所有数据集上获得了高度一致，准确的SWM分析结果，在整个健康和疾病的寿命中都良好的概括。另外，SUPWMA​​的计算速度比其他方法快得多。

高质量注释的医学成像数据集的稀缺性是一个主要问题，它与医学成像分析领域的机器学习应用相撞并阻碍了其进步。自我监督学习是一种最近的培训范式，可以使学习强大的表示无需人类注释，这可以被视为有效的解决方案，以解决带注释的医学数据的稀缺性。本文回顾了自我监督学习方法的最新研究方向，用于图像数据，并将其专注于其在医学成像分析领域的应用。本文涵盖了从计算机视野领域的最新自我监督学习方法，因为它们适用于医学成像分析，并将其归类为预测性，生成性和对比性方法。此外，该文章涵盖了40个在医学成像分析中自学学习领域的最新研究论文，旨在阐明该领域的最新创新。最后，本文以该领域的未来研究指示结束。

我们为RGB视频提供了基于变压器的神经网络体系结构，用于多对象3D重建。它依赖于表示知识的两种替代方法：作为特征的全局3D网格和一系列特定的2D网格。我们通过专用双向注意机制在两者之间逐步交换信息。我们利用有关图像形成过程的知识，以显着稀疏注意力重量矩阵，从而使我们的体系结构在记忆和计算方面可行。我们在3D特征网格的顶部附上一个detr风格的头，以检测场景中的对象并预测其3D姿势和3D形状。与以前的方法相比，我们的体系结构是单阶段，端到端可训练，并且可以从整体上考虑来自多个视频帧的场景，而无需脆弱的跟踪步骤。我们在挑战性的SCAN2CAD数据集上评估了我们的方法，在该数据集中，我们的表现要优于RGB视频的3D对象姿势估算的最新最新方法； （2）将多视图立体声与RGB-D CAD对齐结合的强大替代方法。我们计划发布我们的源代码。

自动检测视网膜结构，例如视网膜血管（RV），凹起的血管区（FAZ）和视网膜血管连接（RVJ），对于了解眼睛的疾病和临床决策非常重要。在本文中，我们提出了一种新型的基于投票的自适应特征融合多任务网络（VAFF-NET），用于在光学相干性层析成像（OCTA）中对RV，FAZ和RVJ进行联合分割，检测和分类。提出了一个特定于任务的投票门模块，以适应并融合两个级别的特定任务的不同功能：来自单个编码器的不同空间位置的特征，以及来自多个编码器的功能。特别是，由于八八座图像中微脉管系统的复杂性使视网膜血管连接连接到分叉/跨越具有挑战性的任务的同时定位和分类，因此我们通过结合热图回归和网格分类来专门设计任务头。我们利用来自各种视网膜层的三个不同的\ textit {en face}血管造影，而不是遵循仅使用单个\ textit {en face}的现有方法。为了促进进一步的研究，已经发布了这些数据集的部分数据集，并已发布了公共访问：https：//github.com/imed-lab/vaff-net。

基于视觉的人类活动识别已成为视频分析领域的重要研究领域之一。在过去的十年中，已经引入了许多先进的深度学习算法，以识别视频流中复杂的人类行为。这些深度学习算法对人类活动识别任务显示出令人印象深刻的表现。但是，这些新引入的方法仅专注于模型性能或这些模型在计算效率和鲁棒性方面的有效性，从而导致其解决挑战性人类活动识别问题的提议中的偏差折衷。为了克服当代深度学习模型对人类活动识别的局限性，本文提出了一个计算高效但通用的空间级联框架，该框架利用了深层歧视性的空间和时间特征，以识别人类活动的识别。为了有效地表示人类行动，我们提出了有效的双重注意卷积神经网络（CNN）体系结构，该结构利用统一的通道空间注意机制来提取视频框架中以人为中心的显着特征。双通道空间注意力层与卷积层一起学会在具有特征图数量的物体的空间接收场中更加专注。然后将提取的判别显着特征转发到堆叠的双向封闭式复发单元（BI-GRU），以使用前进和后传球梯度学习，以实现长期时间建模和对人类行为的识别。进行了广泛的实验，其中获得的结果表明，与大多数当代动作识别方法相比，所提出的框架的执行时间的改善最高167倍。

视频分析的图像分割在不同的研究领域起着重要作用，例如智能城市，医疗保健，计算机视觉和地球科学以及遥感应用。在这方面，最近致力于发展新的细分策略;最新的杰出成就之一是Panoptic细分。后者是由语义和实例分割的融合引起的。明确地，目前正在研究Panoptic细分，以帮助获得更多对视频监控，人群计数，自主驾驶，医学图像分析的图像场景的更细致的知识，以及一般对场景更深入的了解。为此，我们介绍了本文的首次全面审查现有的Panoptic分段方法，以获得作者的知识。因此，基于所采用的算法，应用场景和主要目标的性质，执行现有的Panoptic技术的明确定义分类。此外，讨论了使用伪标签注释新数据集的Panoptic分割。继续前进，进行消融研究，以了解不同观点的Panoptic方法。此外，讨论了适合于Panoptic分割的评估度量，并提供了现有解决方案性能的比较，以告知最先进的并识别其局限性和优势。最后，目前对主题技术面临的挑战和吸引不久的将来吸引相当兴趣的未来趋势，可以成为即将到来的研究研究的起点。提供代码的文件可用于：https：//github.com/elharroussomar/awesome-panoptic-egation

Late-life depression (LLD) is a highly prevalent mood disorder occurring in older adults and is frequently accompanied by cognitive impairment (CI). Studies have shown that LLD may increase the risk of Alzheimer's disease (AD). However, the heterogeneity of presentation of geriatric depression suggests that multiple biological mechanisms may underlie it. Current biological research on LLD progression incorporates machine learning that combines neuroimaging data with clinical observations. There are few studies on incident cognitive diagnostic outcomes in LLD based on structural MRI (sMRI). In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data. Specifically, we first extract prediction-oriented MRI features via a deep neural network, and then integrate them with handcrafted MRI features via a Transformer encoder for cognitive diagnosis prediction. Two tasks are investigated in this work, including (1) identifying cognitively normal subjects with LLD and never-depressed older healthy subjects, and (2) identifying LLD subjects who developed CI (or even AD) and those who stayed cognitively normal over five years. To the best of our knowledge, this is among the first attempts to study the complex heterogeneous progression of LLD based on task-oriented and handcrafted MRI features. We validate the proposed HRL on 294 subjects with T1-weighted MRIs from two clinically harmonized studies. Experimental results suggest that the HRL outperforms several classical machine learning and state-of-the-art deep learning methods in LLD identification and prediction tasks.

X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.