使用深度学习方法（DL）方法的结核病（TB）自动分割（TB） - 一致的病变（CXR）可以帮助减少放射科医生的努力，补充临床决策，并有可能改善患者治疗。文献中的大多数作品使用粗边界框注释讨论培训自动分割模型。但是，边界框注释的粒度可能导致在像素级别上包含相当一部分假阳性和负面因素，从而可能对整体语义分割性能产生不利影响。这项研究（i）评估了使用TB一致性病变的细粒注释和（ii）U-NET模型变体的培训和构造的好处CXR。我们使用多种集合方法（例如位和位或位，位 - 最大值和堆叠）评估了分割性能。我们观察到，与单个组成模型和其他集合方法相比，堆叠合奏表现出优异的分割性能（骰子得分：0.5743，95％置信区间：（0.4055,0.7431））。据我们所知，这是第一个应用合奏学习来改善细粒度元素一致性病变细分性能的研究。

Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases in a clinical decision support system. Current deep learning (DL) models for lung segmentation are trained and evaluated on CXR datasets in which the radiographic projections are captured predominantly from the adult population. However, the shape of the lungs is reported to be significantly different for pediatrics across the developmental stages from infancy to adulthood. This might result in age-related data domain shifts that would adversely impact lung segmentation performance when the models trained on the adult population are deployed for pediatric lung segmentation. In this work, our goal is to analyze the generalizability of deep adult lung segmentation models to the pediatric population and improve performance through a systematic combinatorial approach consisting of CXR modality-specific weight initializations, stacked generalization, and an ensemble of the stacked generalization models. Novel evaluation metrics consisting of Mean Lung Contour Distance and Average Hash Score are proposed in addition to the Multi-scale Structural Similarity Index Measure, Intersection of Union, and Dice metrics to evaluate segmentation performance. We observed a significant improvement (p < 0.05) in cross-domain generalization through our combinatorial approach. This study could serve as a paradigm to analyze the cross-domain generalizability of deep segmentation models for other medical imaging modalities and applications.

胸部X射线（CXR）是一种广泛执行的放射学检查，有助于检测胸腔中组织和器官的异常。检测像Covid-19这样的肺异常可能变得困难，因为它们被像肋和锁骨一样的骨结构的存在模糊，从而导致筛选/诊断误解。自动骨抑制方法有助于抑制这些骨结构并提高软组织可见性。在本研究中，我们建议建立卷积神经网络模型的集合，以抑制正面CXR中的骨骼，提高分类性能，并减少与Covid-19检测相关的解释误差。该合奏由（i）构造（i）测量由前3个执行骨抑制模型和相应子的每个前3个预测的骨抑制图像的子块之间的多尺度结构相似性指数（MS-SSIM）得分 - 其各自的地面真相软组织图像，（ii）执行在每个子块中计算的MS-SSIM分数的大多数投票，以识别具有最大MS-SSIM分数的子块并在构造中使用它最终的骨抑制图像。我们经验确定了提供卓越的骨抑制性能的子块大小。据观察，骨抑制模型集合在MS-SSIM和其他度量方面表现出各个模型。在非骨抑制和骨抑制的图像上再培训和评估特异性特异性分类模型，以将它们分类为显示正常肺部或其他Covid-19类似的表现形式。我们观察到骨抑制的模型训练显着优于非骨抑制图像训练的模型朝着检测Covid-19表现形式。

医学图像通常表现出多种异常。预测它们需要多级分类器，其培训和期望的可靠性性能可能受到因素的组合而影响，例如数据集大小，数据源，分布以及用于训练深度神经网络的损耗功能。目前，跨熵损失仍然是培训深层学习分类器的脱磁场损失功能。然而，这种损失函数断言所有课程的平等学习，导致大多数类的偏见。在这项工作中，我们基准测试适用于多级分类，重点分析模型性能的各种最先进的损失功能，并提出改善的损失功能。我们选择一个小儿胸部X射线（CXR）数据集，其包括没有异常（正常）的图像，以及表现出与细菌和病毒性肺炎一致的表现形式的图像。我们分别构建预测级别和模型级集合，以提高分类性能。我们的结果表明，与个别模型和最先进的文献相比，前3名和前5个模型级集合的预测的加权平均在术语中提供了显着优越的分类性能（P <0.05） MCC（0.9068,95％置信区间（0.8839,0.9297））指标。最后，我们进行了本地化研究，以解释模型行为，以便可视化和确认个人模型和集合学习有意义的特征和突出显示的疾病表现。

The devastation caused by the coronavirus pandemic makes it imperative to design automated techniques for a fast and accurate detection. We propose a novel non-invasive tool, using deep learning and imaging, for delineating COVID-19 infection in lungs. The Ensembling Attention-based Multi-scaled Convolution network (EAMC), employing Leave-One-Patient-Out (LOPO) training, exhibits high sensitivity and precision in outlining infected regions along with assessment of severity. The Attention module combines contextual with local information, at multiple scales, for accurate segmentation. Ensemble learning integrates heterogeneity of decision through different base classifiers. The superiority of EAMC, even with severe class imbalance, is established through comparison with existing state-of-the-art learning models over four publicly-available COVID-19 datasets. The results are suggestive of the relevance of deep learning in providing assistive intelligence to medical practitioners, when they are overburdened with patients as in pandemics. Its clinical significance lies in its unprecedented scope in providing low-cost decision-making for patients lacking specialized healthcare at remote locations.

内窥镜图像通常包含几个伪像。伪影显着影响图像分析导致计算机辅助诊断。卷积神经网络（CNNS），一种深度学习，可以去除这样的伪像。已经提出了各种架构，用于CNNS，并且伪像去除的准确性根据架构的选择而变化。因此，需要根据所选择的架构确定伪影删除精度。在这项研究中，我们专注于内窥镜手术器械作为伪影，并使用七种不同的CNN架构确定和讨论伪影去除精度。

医学图像分割的主要困难之一是这些图像的高可变性，这是由它们的起源（多中心），获取协议（多参数）以及人类解剖学的可变性引起的，以及人的严重程度疾病，年龄和性别的影响等。在这项工作中解决的问题是使用卷积神经网络的腰椎磁共振图像的自动语义分割。目的是为图像的每个像素分配类标签。课程由放射科学家定义，对应于不同的结构元素，如椎骨，椎间盘，神经，血管和其他组织。所提出的网络拓扑是U-Net架构的变体。几个互补块用于定义变体：三种类型的卷积块，空间注意模型，深度监督和多级特征提取器。本文档描述了拓扑，并分析了获得最准确的细分的神经网络设计的结果。其中一些建议的设计优于使用作为基线的标准U-Net，特别是当在与不同的策略组合的多个神经网络的输出中使用的集合时使用。

Pneumonia, a respiratory infection brought on by bacteria or viruses, affects a large number of people, especially in developing and impoverished countries where high levels of pollution, unclean living conditions, and overcrowding are frequently observed, along with insufficient medical infrastructure. Pleural effusion, a condition in which fluids fill the lung and complicate breathing, is brought on by pneumonia. Early detection of pneumonia is essential for ensuring curative care and boosting survival rates. The approach most usually used to diagnose pneumonia is chest X-ray imaging. The purpose of this work is to develop a method for the automatic diagnosis of bacterial and viral pneumonia in digital x-ray pictures. This article first presents the authors' technique, and then gives a comprehensive report on recent developments in the field of reliable diagnosis of pneumonia. In this study, here tuned a state-of-the-art deep convolutional neural network to classify plant diseases based on images and tested its performance. Deep learning architecture is compared empirically. VGG19, ResNet with 152v2, Resnext101, Seresnet152, Mobilenettv2, and DenseNet with 201 layers are among the architectures tested. Experiment data consists of two groups, sick and healthy X-ray pictures. To take appropriate action against plant diseases as soon as possible, rapid disease identification models are preferred. DenseNet201 has shown no overfitting or performance degradation in our experiments, and its accuracy tends to increase as the number of epochs increases. Further, DenseNet201 achieves state-of-the-art performance with a significantly a smaller number of parameters and within a reasonable computing time. This architecture outperforms the competition in terms of testing accuracy, scoring 95%. Each architecture was trained using Keras, using Theano as the backend.

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.

语义分割包括通过将其分配给从一组所有可用的标签来分类图像的每个像素。在过去的几年里，很多关注转移到这种任务。许多计算机视觉研究人员试图应用AutoEncoder结构来开发可以学习图像语义的模型以及它的低级表示。在给定输入的AutoEncoder架构中，编码器计算的输入的低维表示，然后解码器用于重建原始数据。在这项工作中，我们提出了一个卷积神经网络（CNNS）的集合。在集合方法中，许多不同的型号训练，然后用于分类，整体汇总了单个分类器的输出。该方法利用各种分类器的差异来提高整个系统的性能。通过使用不同的丢失函数强制执行单个分类器中的多样性。特别是，我们提出了一种新的损失函数，从骰子和结构相似度指数的组合产生。通过使用Deeplabv3 +和Hardnet环境结合不同的骨干网络来实现所提出的合奏。该提案是通过关于两个真实情景的广泛实证评估来评估：息肉和皮肤细分。所有代码都在HTTPS://github.com/lorisnanni在线提供。

Determination of treatment need of posterior capsular opacification (PCO)-- one of the most common complication of cataract surgery -- is a difficult process due to its local unavailability and the fact that treatment is provided only after PCO occurs in the central visual axis. In this paper we propose a deep learning (DL)-based method to first segment PCO images then classify the images into \textit{treatment required} and \textit{not yet required} cases in order to reduce frequent hospital visits. To train the model, we prepare a training image set with ground truths (GT) obtained from two strategies: (i) manual and (ii) automated. So, we have two models: (i) Model 1 (trained with image set containing manual GT) (ii) Model 2 (trained with image set containing automated GT). Both models when evaluated on validation image set gave Dice coefficient value greater than 0.8 and intersection-over-union (IoU) score greater than 0.67 in our experiments. Comparison between gold standard GT and segmented results from our models gave a Dice coefficient value greater than 0.7 and IoU score greater than 0.6 for both the models showing that automated ground truths can also result in generation of an efficient model. Comparison between our classification result and clinical classification shows 0.98 F2-score for outputs from both the models.

检测新的多发性硬化症（MS）病变是该疾病进化的重要标志。基于学习的方法的适用性可以有效地自动化此任务。然而，缺乏带有新型病变的注释纵向数据是训练健壮和概括模型的限制因素。在这项工作中，我们描述了一条基于学习的管道，该管道解决了检测和细分新MS病变的挑战性任务。首先，我们建议使用单个时间点对在分割任务进行训练的模型中使用转移学习。因此，我们从更轻松的任务中利用知识，并为此提供更多注释的数据集。其次，我们提出了一种数据综合策略，以使用单个时间点扫描生成新的纵向时间点。通过这种方式，我们将检测模型预算到大型合成注释数据集上。最后，我们使用旨在模拟MRI中数据多样性的数据实践技术。通过这样做，我们增加了可用的小注释纵向数据集的大小。我们的消融研究表明，每个贡献都会提高分割精度。使用拟议的管道，我们获得了MSSEG2 MICCAI挑战中新的MS病变的分割和检测的最佳分数。

对骨关节炎（OA）的磁共振成像（MRI）扫描的客观评估可以解决当前OA评估的局限性。 OA客观评估是必需的骨，软骨和关节液的分割。大多数提出的分割方法都不执行实例分割，并且遭受了类不平衡问题。这项研究部署了蒙版R-CNN实例分割并改进了IT（改进的面罩R-CNN（IMASKRCNN）），以获得与OA相关组织的更准确的广义分割。该方法的训练和验证是使用骨关节炎倡议（OAI）数据集的500次MRI膝盖和有症状髋关节OA患者的97次MRI扫描进行的。掩盖R-CNN的三个修改产生了iMaskRCNN：添加第二个Roialigned块，在掩码标先中添加了额外的解码器层，并通过跳过连接将它们连接起来。使用Hausdorff距离，骰子评分和变异系数（COV）评估结果。与面膜RCNN相比，iMaskRCNN导致骨骼和软骨分割的改善，这表明股骨的骰子得分从95％增加到98％，胫骨的95％到97％，股骨软骨的71％至80％，81％和81％胫骨软骨的％至82％。对于积液检测，iMaskRCNN 72％比MaskRCNN 71％改善了骰子。 Reader1和Mask R-CNN（0.33），Reader1和ImaskRCNN（0.34），Reader2和Mask R-CNN（0.22），Reader2和iMaskRCNN（0.29）之间的积液检测的COV值（0.34），读取器2和mask r-CNN（0.22）接近COV之间，表明人类读者与蒙版R-CNN和ImaskRCNN之间的一致性很高。蒙版R-CNN和ImaskRCNN可以可靠，同时提取与OA有关的不同规模的关节组织，从而为OA的自动评估构成基础。 iMaskRCNN结果表明，修改改善了边缘周围的网络性能。

Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge -- Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application.

Medical image segmentation is an actively studied task in medical imaging, where the precision of the annotations is of utter importance towards accurate diagnosis and treatment. In recent years, the task has been approached with various deep learning systems, among the most popular models being U-Net. In this work, we propose a novel strategy to generate ensembles of different architectures for medical image segmentation, by leveraging the diversity (decorrelation) of the models forming the ensemble. More specifically, we utilize the Dice score among model pairs to estimate the correlation between the outputs of the two models forming each pair. To promote diversity, we select models with low Dice scores among each other. We carry out gastro-intestinal tract image segmentation experiments to compare our diversity-promoting ensemble (DiPE) with another strategy to create ensembles based on selecting the top scoring U-Net models. Our empirical results show that DiPE surpasses both individual models as well as the ensemble creation strategy based on selecting the top scoring models.

本文提出了第二版的头部和颈部肿瘤（Hecktor）挑战的概述，作为第24届医学图像计算和计算机辅助干预（Miccai）2021的卫星活动。挑战由三个任务组成与患有头颈癌（H＆N）的患者的PET / CT图像的自动分析有关，专注于oropharynx地区。任务1是FDG-PET / CT图像中H＆N主肿瘤肿瘤体积（GTVT）的自动分割。任务2是来自同一FDG-PET / CT的进展自由生存（PFS）的自动预测。最后，任务3与任务2的任务2与参与者提供的地面真理GTVT注释相同。这些数据从六个中心收集，总共325个图像，分为224个培训和101个测试用例。通过103个注册团队和448个结果提交的重要参与，突出了对挑战的兴趣。在第一任务中获得0.7591的骰子相似度系数（DSC），分别在任务2和3中的0.7196和0.6978的一致性指数（C-Index）。在所有任务中，发现这种方法的简单性是确保泛化性能的关键。 PFS预测性能在任务2和3中的比较表明，提供GTVT轮廓对于实现最佳结果，这表明可以使用完全自动方法。这可能避免了对GTVT轮廓的需求，用于可重复和大规模的辐射瘤研究的开头途径，包括千元潜在的受试者。

To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8,403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was done using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,{\theta}) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71+/-0.10 and pixel-wise sensitivity/specificity of 87.7+/-6.6%/99.8+/-0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5+/-0.3%, specificity of 98.8+/-1.0%, and accuracy of 99.1+/-0.5%. The classification step eliminated the majority of residual false positives, and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared to 730 from manual analysis, representing a 4.4% difference. When compared to the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.

尽管近期基于深度学习的语义细分，但远程感测图像的自动建筑检测仍然是一个具有挑战性的问题，由于全球建筑物的出现巨大变化。误差主要发生在构建足迹的边界，阴影区域，以及检测外表面具有与周围区域非常相似的反射率特性的建筑物。为了克服这些问题，我们提出了一种生成的对抗基于网络的基于网络的分割框架，其具有嵌入在发电机中的不确定性关注单元和改进模块。由边缘和反向关注单元组成的细化模块，旨在精炼预测的建筑地图。边缘注意力增强了边界特征，以估计更高的精度，并且反向关注允许网络探索先前估计区域中缺少的功能。不确定性关注单元有助于网络解决分类中的不确定性。作为我们方法的权力的衡量标准，截至2021年12月4日，它在Deepglobe公共领导板上的第二名，尽管我们的方法的主要重点 - 建筑边缘 - 并不完全对齐用于排行榜排名的指标。 DeepGlobe充满挑战数据集的整体F1分数为0.745。我们还报告了对挑战的Inria验证数据集的最佳成绩，我们的网络实现了81.28％的总体验证，总体准确性为97.03％。沿着同一条线，对于官方Inria测试数据集，我们的网络总体上得分77.86％和96.41％，而且准确性。

前列腺癌是美国男人的第二致致命癌症。虽然磁共振成像（MRI）越来越多地用于引导前列腺癌诊断的靶向活组织检查，但其效用仍然受到限制，因为假阳性和假否定的高率以及较低的读者协议。机器学习方法在前列腺MRI上检测和定位癌症可以帮助标准化放射科学诠释。然而，现有的机器学习方法不仅在模型架构中不等，而且还可以在用于模型培训的地面真理标签策略中。在这项研究中，我们比较不同的标记策略，即病理证实放射科标签，整个安装组织病理学图像上的病理学家标签，以及病变水平和像素级数字病理学家标签（先前验证了组织病理学图像上的深层学习算法以预测像素 - 整个安装组织病理学图像上的Gleason模式）。我们分析这些标签对训练有素的机器学习模型的性能的影响。我们的实验表明，用它们培训的（1）放射科标签和模型可能会错过癌症，或低估癌症程度，（2）与他们培训的数字病理学家标签和模型与病理学家标签有高度的一致性，而（3）用数字病理学家培训的模型标签在两种不同疾病分布的两种不同群组中达到最佳性能，而不管使用的模型建筑如何。数字病理学家标签可以减少与人类注释相关的挑战，包括劳动力，时间，和读者间变异性，并且可以通过使可靠的机器学习模型进行培训来检测和定位前列腺癌，帮助弥合前列腺放射学和病理学之间的差距在MRI。