恢复质量差的图像与一组混合伪影对于可靠的诊断起着至关重要的作用。现有的研究集中在特定的恢复问题上,例如图像过度,去核和暴露校正,通常对伪影类型和严重性有很强的假设。作为盲X射线恢复的先驱研究,我们提出了一个通用图像恢复和分类的联合模型:恢复分类为分类的生成对抗网络(R2C-GAN)。这种共同优化的模型使恢复后保持任何疾病完整。因此,由于X射线图像质量的提高,这自然会导致更高的诊断性能。为了实现这一关键目标,我们将恢复任务定义为图像到图像的翻译问题,从差异,模糊或暴露不足/暴露不足的图像到高质量的图像域。提出的R2C-GAN模型能够使用未配对的训练样本在两个域之间学习前进和逆变换。同时,联合分类在恢复过程中保留了疾病标签。此外,R2C-GAN配备了操作层/神经元,可降低网络深度,并进一步增强恢复和分类性能。拟议的联合模型对2019年冠状病毒病(COVID-19)分类的卡塔-COV19数据集进行了广泛的评估。拟议的恢复方法达到了90%以上的F1得分,这显着高于任何深层模型的性能。此外,在定性分析中,R2C-GAN的恢复性能得到了一群医生的批准。我们在https://github.com/meteahishali/r2c-gan上共享软件实施。
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Objective: Despite numerous studies proposed for audio restoration in the literature, most of them focus on an isolated restoration problem such as denoising or dereverberation, ignoring other artifacts. Moreover, assuming a noisy or reverberant environment with limited number of fixed signal-to-distortion ratio (SDR) levels is a common practice. However, real-world audio is often corrupted by a blend of artifacts such as reverberation, sensor noise, and background audio mixture with varying types, severities, and duration. In this study, we propose a novel approach for blind restoration of real-world audio signals by Operational Generative Adversarial Networks (Op-GANs) with temporal and spectral objective metrics to enhance the quality of restored audio signal regardless of the type and severity of each artifact corrupting it. Methods: 1D Operational-GANs are used with generative neuron model optimized for blind restoration of any corrupted audio signal. Results: The proposed approach has been evaluated extensively over the benchmark TIMIT-RAR (speech) and GTZAN-RAR (non-speech) datasets corrupted with a random blend of artifacts each with a random severity to mimic real-world audio signals. Average SDR improvements of over 7.2 dB and 4.9 dB are achieved, respectively, which are substantial when compared with the baseline methods. Significance: This is a pioneer study in blind audio restoration with the unique capability of direct (time-domain) restoration of real-world audio whilst achieving an unprecedented level of performance for a wide SDR range and artifact types. Conclusion: 1D Op-GANs can achieve robust and computationally effective real-world audio restoration with significantly improved performance. The source codes and the generated real-world audio datasets are shared publicly with the research community in a dedicated GitHub repository1.
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新的冠状病毒造成了超过一百万的死亡,并继续迅速传播。这种病毒靶向肺部,导致呼吸窘迫,这可以轻度或严重。肺的X射线或计算机断层扫描(CT)图像可以揭示患者是否感染Covid-19。许多研究人员正在尝试使用人工智能改善Covid-19检测。我们的动机是开发一种可以应对的自动方法,该方法可以应对标记数据的方案是耗时或昂贵的。在本文中,我们提出了使用依赖于Sobel边缘检测和生成对冲网络(GANS)的有限标记数据(SCLLD)的半监督分类来自动化Covid-19诊断。 GaN鉴别器输出是一种概率值,用于在这项工作中进行分类。建议的系统使用从Omid Hosparing收集的10,000 CT扫描培训,而公共数据集也用于验证我们的系统。将该方法与其他最先进的监督方法进行比较,例如高斯过程。据我们所知,这是第一次提出了对Covid-19检测的半监督方法。我们的系统能够从有限标记和未标记数据的混合学习,该数据由于缺乏足够量的标记数据而导致的监督学习者失败。因此,我们的半监督训练方法显着优于卷积神经网络(CNN)的监督培训,当标记的训练数据稀缺时。在精度,敏感性和特异性方面,我们的方法的95%置信区间分别为99.56±0.20%,99.88±0.24%和99.40±0.1.18%,而CNN的间隔(训练有素的监督)为68.34 + - 4.11%,91.2 + - 6.15%,46.40 + - 5.21%。
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Deep learning (DL) analysis of Chest X-ray (CXR) and Computed tomography (CT) images has garnered a lot of attention in recent times due to the COVID-19 pandemic. Convolutional Neural Networks (CNNs) are well suited for the image analysis tasks when trained on humongous amounts of data. Applications developed for medical image analysis require high sensitivity and precision compared to any other fields. Most of the tools proposed for detection of COVID-19 claims to have high sensitivity and recalls but have failed to generalize and perform when tested on unseen datasets. This encouraged us to develop a CNN model, analyze and understand the performance of it by visualizing the predictions of the model using class activation maps generated using (Gradient-weighted Class Activation Mapping) Grad-CAM technique. This study provides a detailed discussion of the success and failure of the proposed model at an image level. Performance of the model is compared with state-of-the-art DL models and shown to be comparable. The data and code used are available at https://github.com/aleesuss/c19.
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在图像识别中已广泛提出了生成模型,以生成更多图像,其中分布与真实图像相似。它通常会引入一个歧视网络,以区分真实数据与生成的数据。这样的模型利用了一个歧视网络,该网络负责以区分样式从目标数据集中包含的数据传输的数据。但是,这样做的网络着重于强度分布的差异,并可能忽略数据集之间的结构差异。在本文中,我们制定了一个新的图像到图像翻译问题,以确保生成的图像的结构类似于目标数据集中的图像。我们提出了一个简单但功能强大的结构不稳定的对抗(SUA)网络,该网络在执行图像分割时介绍了训练和测试集之间的强度和结构差异。它由空间变换块组成,然后是强度分布渲染模块。提出了空间变换块来减少两个图像之间的结构缝隙,还产生了一个反变形字段,以使最终的分段图像背部扭曲。然后,强度分布渲染模块将变形结构呈现到具有目标强度分布的图像。实验结果表明,所提出的SUA方法具有在多个数据集之间传递强度分布和结构含量的能力。
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2019年12月,一个名为Covid-19的新型病毒导致了迄今为止的巨大因果关系。与新的冠状病毒的战斗在西班牙语流感后令人振奋和恐怖。虽然前线医生和医学研究人员在控制高度典型病毒的传播方面取得了重大进展,但技术也证明了在战斗中的重要性。此外,许多医疗应用中已采用人工智能,以诊断许多疾病,甚至陷入困境的经验丰富的医生。因此,本调查纸探讨了提议的方法,可以提前援助医生和研究人员,廉价的疾病诊断方法。大多数发展中国家难以使用传统方式进行测试,但机器和深度学习可以采用显着的方式。另一方面,对不同类型的医学图像的访问已经激励了研究人员。结果,提出了一种庞大的技术数量。本文首先详细调了人工智能域中传统方法的背景知识。在此之后,我们会收集常用的数据集及其用例日期。此外,我们还显示了采用深入学习的机器学习的研究人员的百分比。因此,我们对这种情况进行了彻底的分析。最后,在研究挑战中,我们详细阐述了Covid-19研究中面临的问题,我们解决了我们的理解,以建立一个明亮健康的环境。
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In biomedical image analysis, the applicability of deep learning methods is directly impacted by the quantity of image data available. This is due to deep learning models requiring large image datasets to provide high-level performance. Generative Adversarial Networks (GANs) have been widely utilized to address data limitations through the generation of synthetic biomedical images. GANs consist of two models. The generator, a model that learns how to produce synthetic images based on the feedback it receives. The discriminator, a model that classifies an image as synthetic or real and provides feedback to the generator. Throughout the training process, a GAN can experience several technical challenges that impede the generation of suitable synthetic imagery. First, the mode collapse problem whereby the generator either produces an identical image or produces a uniform image from distinct input features. Second, the non-convergence problem whereby the gradient descent optimizer fails to reach a Nash equilibrium. Thirdly, the vanishing gradient problem whereby unstable training behavior occurs due to the discriminator achieving optimal classification performance resulting in no meaningful feedback being provided to the generator. These problems result in the production of synthetic imagery that is blurry, unrealistic, and less diverse. To date, there has been no survey article outlining the impact of these technical challenges in the context of the biomedical imagery domain. This work presents a review and taxonomy based on solutions to the training problems of GANs in the biomedical imaging domain. This survey highlights important challenges and outlines future research directions about the training of GANs in the domain of biomedical imagery.
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Deep Convolutional Neural Networks (DCNNs) have exhibited impressive performance on image super-resolution tasks. However, these deep learning-based super-resolution methods perform poorly in real-world super-resolution tasks, where the paired high-resolution and low-resolution images are unavailable and the low-resolution images are degraded by complicated and unknown kernels. To break these limitations, we propose the Unsupervised Bi-directional Cycle Domain Transfer Learning-based Generative Adversarial Network (UBCDTL-GAN), which consists of an Unsupervised Bi-directional Cycle Domain Transfer Network (UBCDTN) and the Semantic Encoder guided Super Resolution Network (SESRN). First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain. Second, the SESRN has the ability to super-resolve the approximated real-like LR image to a photo-realistic HR image. Extensive experiments on unpaired real-world image benchmark datasets demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods.
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眼底图像对于鉴定各种眼科疾病非常有用。然而,由于伪影存在,视网膜的可见性受到严重影响。这可能导致可能导致更复杂的问题的疾病误诊。由于深度学习是一种强大的工具,可以在没有人为干预的情况下从数据中提取模式,它们可以应用于图像到图像图像到图像的翻译问题。本文已经尝试了,以自动纠正眼底图像中存在的伪像。我们使用基于Cnercangan的模型,该模型由残余块组成,以减少图像中的伪影。与现有技术相比,可以看到显着的改进。
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Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, superresolution, and inpainting. Furthermore, the same prior can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash input pairs.
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Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the recent deep learning (DL) methodologies trained on the labeled data from the same machine, the classification accuracy significantly deteriorates when one or few conditions are altered. Furthermore, their performance suffers significantly or may entirely fail when they are tested on another machine with entirely different healthy and faulty signal patterns. To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics. To accomplish this objective, a 1D Operational Generative Adversarial Network (Op-GAN) first characterizes the transition between normal and fault vibration signals of (a) source machine(s) under various conditions, sensor parameters, and fault types. Then for a target machine, the potential faulty signals can be generated, and over its actual healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN fault detector can then be trained to detect the real faulty condition in real time whenever it occurs. To validate the proposed approach, a new benchmark dataset is created using two different motors working under different conditions and sensor locations. Experimental results demonstrate that this novel approach can accurately detect any bearing fault achieving an average recall rate of around 89% and 95% on two target machines regardless of its type, severity, and location.
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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.
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有必要开发负担得起且可靠的诊断工具,该工具允许包含COVID-19的扩散。已经提出了机器学习(ML)算法来设计支持决策系统以评估胸部X射线图像,事实证明,这些图像可用于检测和评估疾病进展。许多研究文章围绕此主题发表,这使得很难确定未来工作的最佳方法。本文介绍了使用胸部X射线图像应用于COVID-19检测的ML的系统综述,旨在就方法,体系结构,数据库和当前局限性为研究人员提供基线。
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The success of deep learning is largely due to the availability of large amounts of training data that cover a wide range of examples of a particular concept or meaning. In the field of medicine, having a diverse set of training data on a particular disease can lead to the development of a model that is able to accurately predict the disease. However, despite the potential benefits, there have not been significant advances in image-based diagnosis due to a lack of high-quality annotated data. This article highlights the importance of using a data-centric approach to improve the quality of data representations, particularly in cases where the available data is limited. To address this "small-data" issue, we discuss four methods for generating and aggregating training data: data augmentation, transfer learning, federated learning, and GANs (generative adversarial networks). We also propose the use of knowledge-guided GANs to incorporate domain knowledge in the training data generation process. With the recent progress in large pre-trained language models, we believe it is possible to acquire high-quality knowledge that can be used to improve the effectiveness of knowledge-guided generative methods.
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这项研究的目的是开发一个强大的基于深度学习的框架,以区分Covid-19,社区获得的肺炎(CAP)和基于使用各种方案和放射剂量在不同成像中心获得的胸部CT扫描的正常病例和正常情况。我们表明,虽然我们的建议模型是在使用特定扫描协议仅从一个成像中心获取的相对较小的数据集上训练的,但该模型在使用不同技术参数的多个扫描仪获得的异质测试集上表现良好。我们还表明,可以通过无监督的方法来更新模型,以应对火车和测试集之间的数据移动,并在从其他中心接收新的外部数据集时增强模型的鲁棒性。我们采用了合奏体系结构来汇总该模型的多个版本的预测。为了初始培训和开发目的,使用了171 Covid-19、60 CAP和76个正常情况的内部数据集,其中包含使用恒定的标准辐射剂量扫描方案从一个成像中心获得的体积CT扫描。为了评估模型,我们回顾了四个不同的测试集,以研究数据特征对模型性能的转移的影响。在测试用例中,有与火车组相似的CT扫描,以及嘈杂的低剂量和超低剂量CT扫描。此外,从患有心血管疾病或手术病史的患者中获得了一些测试CT扫描。这项研究中使用的整个测试数据集包含51 covid-19、28 CAP和51例正常情况。实验结果表明,我们提出的框架在所有测试集上的表现良好,达到96.15%的总准确度(95%CI:[91.25-98.74]),COVID-119,COVID-96.08%(95%CI:[86.54-99.5],95%),[86.54-99.5],),,),敏感性。帽敏感性为92.86%(95%CI:[76.50-99.19])。
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每年有大约4.5亿人受到肺炎的影响,导致250万人死亡。 Covid-19也影响了1.81亿人,这导致了392万人伤亡。如果早期诊断,两种疾病死亡可能会显着降低。然而,目前诊断肺炎(投诉+胸部X射线)和Covid-19(RT-PCR)的方法分别存在专家放射科医生和时间。在深度学习模型的帮助下,可以从胸部X射线或CT扫描立即检测肺炎和Covid-19。这样,诊断肺炎/ Covid-19的过程可以更有效和普遍地制作。在本文中,我们的目标是引出,解释和评估,定性和定量,深入学习方法的主要进步,旨在检测或定位社区获得的肺炎(帽),病毒肺炎和Covid-19从胸部X-的图像光线和CT扫描。作为一个系统的审查,本文的重点在于解释了深度学习模型架构,该架构已经被修改或从划痕,以便WIWTH对概括性的关注。对于每个模型,本文回答了模型所设计的方式的问题,特定模型克服的挑战以及修改模型到所需规格的折衷。还提供了本文描述的所有模型的定量分析,以量化不同模型的有效性与相似的目标。一些权衡无法量化,因此它们在定性分析中明确提到,在整个纸张中完成。通过在一个地方编译和分析大量的研究细节,其中包含所有数据集,模型架构和结果,我们的目标是为对此字段感兴趣的初学者和当前研究人员提供一站式解决方案。
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本文提出了图像恢复的新变异推理框架和一个卷积神经网络(CNN)结构,该结构可以解决所提出的框架所描述的恢复问题。较早的基于CNN的图像恢复方法主要集中在网络体系结构设计或培训策略上,具有非盲方案,其中已知或假定降解模型。为了更接近现实世界的应用程序,CNN还接受了整个数据集的盲目培训,包括各种降解。然而,给定有多样化的图像的高质量图像的条件分布太复杂了,无法通过单个CNN学习。因此,也有一些方法可以提供其他先验信息来培训CNN。与以前的方法不同,我们更多地专注于基于贝叶斯观点以及如何重新重新重构目标的恢复目标。具体而言,我们的方法放松了原始的后推理问题,以更好地管理子问题,因此表现得像分裂和互动方案。结果,与以前的框架相比,提出的框架提高了几个恢复问题的性能。具体而言,我们的方法在高斯denoising,现实世界中的降噪,盲图超级分辨率和JPEG压缩伪像减少方面提供了最先进的性能。
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经典图像去噪方法利用非本地自相似原理来有效地从嘈杂的图像中恢复图像内容。目前的最先进的方法使用深卷积神经网络(CNNS),以有效地学习从嘈杂到清洁图像的映射。深度去噪CNNS表现出高学习能力,并集成了由于大量隐藏层所产生的大型接收领域而整合非本地信息。然而,深网络也是计算复杂的并且需要大数据进行培训。为了解决这些问题,本研究旨在通过一种新的神经元模型赋予自组织的操作神经网络(自我onns)的重点,该模型可以通过紧凑且浅的模型实现类似或更好的去噪性能。最近,已经引入了超神经元的概念,其通过利用未局限性的内核位置来增强生成神经元的非线性变换,以获得增强的接受场大小。这是赋予深度网络配置需求的关键成就。由于已知非本地信息的整合受益于去噪,在这项工作中,我们研究了超神经元对合成和现实世界图像去噪的使用。我们还讨论了在GPU上实施超神经元模型的实际问题,并提出了非本地化操作的异质性与计算复杂性之间的权衡。我们的结果表明,具有相同的宽度和深度,具有超级神经元的自动onn,具有对具有生成和卷积神经元的网络的去噪性能,为脱结任务提供了显着的促进。此外,结果表明,具有超神经元的自串,可以分别为合成和真实世界的众所周知的众所周知的深层CNN去噪者达到竞争和优越的合成表演。
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最近,由于高性能,深度学习方法已成为生物学图像重建和增强问题的主要研究前沿,以及其超快速推理时间。但是,由于获得监督学习的匹配参考数据的难度,对不需要配对的参考数据的无监督学习方法越来越兴趣。特别是,已成功用于各种生物成像应用的自我监督的学习和生成模型。在本文中,我们概述了在古典逆问题的背景下的连贯性观点,并讨论其对生物成像的应用,包括电子,荧光和去卷积显微镜,光学衍射断层扫描和功能性神经影像。
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生成的对抗网络(GANS)已经促进了解决图像到图像转换问题的新方向。不同的GANS在目标函数中使用具有不同损耗的发电机和鉴别器网络。仍然存在差距来填补所生成的图像的质量并靠近地面真理图像。在这项工作中,我们介绍了一个名为循环辨别生成的对抗网络(CDGAN)的新的图像到图像转换网络,填补了上述空白。除了加速本的原始架构之外,所提出的CDGAN通过结合循环图像的附加鉴别器网络来产生高质量和更现实的图像。所提出的CDGAN在三个图像到图像转换数据集上进行测试。分析了定量和定性结果,并与最先进的方法进行了比较。在三个基线图像到图像转换数据集中,所提出的CDGAN方法优于最先进的方法。该代码可在https://github.com/kishankancharagunta/cdgan获得。
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