Metric learning aims to learn distances from the data, which enhances the performance of similarity-based algorithms. An author style detection task is a metric learning problem, where learning style features with small intra-class variations and larger inter-class differences is of great importance to achieve better performance. Recently, metric learning based on softmax loss has been used successfully for style detection. While softmax loss can produce separable representations, its discriminative power is relatively poor. In this work, we propose NBC-Softmax, a contrastive loss based clustering technique for softmax loss, which is more intuitive and able to achieve superior performance. Our technique meets the criterion for larger number of samples, thus achieving block contrastiveness, which is proven to outperform pair-wise losses. It uses mini-batch sampling effectively and is scalable. Experiments on 4 darkweb social forums, with NBCSAuthor that uses the proposed NBC-Softmax for author and sybil detection, shows that our negative block contrastive approach constantly outperforms state-of-the-art methods using the same network architecture. Our code is publicly available at : https://github.com/gayanku/NBC-Softmax
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In medical image analysis, automated segmentation of multi-component anatomical structures, which often have a spectrum of potential anomalies and pathologies, is a challenging task. In this work, we develop a multi-step approach using U-Net-based neural networks to initially detect anomalies (bone marrow lesions, bone cysts) in the distal femur, proximal tibia and patella from 3D magnetic resonance (MR) images of the knee in individuals with varying grades of osteoarthritis. Subsequently, the extracted data are used for downstream tasks involving semantic segmentation of individual bone and cartilage volumes as well as bone anomalies. For anomaly detection, the U-Net-based models were developed to reconstruct the bone profiles of the femur and tibia in images via inpainting so anomalous bone regions could be replaced with close to normal appearances. The reconstruction error was used to detect bone anomalies. A second anomaly-aware network, which was compared to anomaly-na\"ive segmentation networks, was used to provide a final automated segmentation of the femoral, tibial and patellar bones and cartilages from the knee MR images containing a spectrum of bone anomalies. The anomaly-aware segmentation approach provided up to 58% reduction in Hausdorff distances for bone segmentations compared to the results from the anomaly-na\"ive segmentation networks. In addition, the anomaly-aware networks were able to detect bone lesions in the MR images with greater sensitivity and specificity (area under the receiver operating characteristic curve [AUC] up to 0.896) compared to the anomaly-na\"ive segmentation networks (AUC up to 0.874).
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对比学习最近在包括图形在内的许多领域取得了巨大的成功。然而,对比损失,尤其是对于图形,需要大量的负样本,这些样本是不可计算的,并且在二次时复杂性具有计算性过高。子采样不是最佳和不正确的负抽样导致采样偏差。在这项工作中,我们提出了一种基于元节点的近似技术,该技术可以(a)代理二次群集大小的时间复杂性中的所有负组合(b),(c)在图级别,而不是节点级别,(d)利用图形稀疏性。通过用添加群集对替换节点对,我们在图表级别计算群集时间的负fertiations。最终的代理近似元节点对比度(PAMC)损失基于简单优化的GPU操作,可捕获完整的负面因素,但具有线性时间复杂性,但具有有效的效率。通过避免采样,我们有效地消除了样本偏差。我们符合大量样品的标准,从而实现了块对比度,这被证明超过了成对的损失。我们使用学习的软群集分配进行元节点收缩,并避免在边缘创建过程中添加可能的异质和噪声。从理论上讲,我们表明现实世界图表很容易满足我们近似所需的条件。从经验上讲,我们在6个基准测试上表现出对最先进的图形聚类的有希望的准确性。重要的是,我们在效率方面获得了可观的收益。训练时间最多可达3倍,推理时间为1.8倍,减少GPU记忆的时间超过5倍。
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实际上,许多医疗数据集在疾病标签空间上定义了基本的分类学。但是,现有的医学诊断分类算法通常假定具有语义独立的标签。在这项研究中,我们旨在利用深度学习算法来利用类层次结构,以更准确,可靠的皮肤病变识别。我们提出了一个双曲线网络,以共同学习图像嵌入和类原型。事实证明,双曲线为与欧几里得几何形状更好地建模层次关系提供了一个空间。同时,我们使用从类层次结构编码的距离矩阵限制双曲线原型的分布。因此,学习的原型保留了嵌入空间中的语义类关系,我们可以通过将图像特征分配给最近的双曲线类原型来预测图像的标签。我们使用内部皮肤病变数据集,该数据集由65种皮肤疾病的大约230k皮肤镜图像组成,以验证我们的方法。广泛的实验提供了证据表明,与模型相比,我们的模型可以实现更高的准确性,而在不考虑班级关系的情况下可以实现更高的严重分类错误。
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与传统的手工制作方法相比,卷积神经网络(CNN)表现出出色的压缩感测(CS)性能。但是,它们在通用性,归纳偏见和难以建模长距离关系方面受到了广泛的限制。变压器神经网络(TNN)通过实施旨在捕获输入之间依赖性的注意机制来克服此类问题。但是,高分辨率任务通常需要视觉变压器(VIT)将图像分解为基于贴片的令牌,将输入限制为固有的本地环境。我们提出了一种新型的图像分解,将图像自然嵌入到低分辨率输入中。这些万花筒令牌(KD)以与基于贴片的方法相同的计算成本提供了一种全球关注的机制。为了展示这一发展,我们用TNN块替换了众所周知的CS-MRI神经网络中的CNN组件,并证明了KD提供的改进。我们还提出了图像令牌的合奏,从而提高了整体图像质量并降低了模型大小。提供补充材料:https://github.com/uqmarlonbran/tcs.git
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Data scarcity is common in deep learning models for medical image segmentation. Previous works proposed multi-dataset learning, either simultaneously or via transfer learning to expand training sets. However, medical image datasets have diverse-sized images and features, and developing a model simultaneously for multiple datasets is challenging. This work proposes Fabric Image Representation Encoding Network (FIRENet), a universal architecture for simultaneous multi-dataset segmentation and transfer learning involving arbitrary numbers of dataset(s). To handle different-sized image and feature, a 3D fabric module is used to encapsulate many multi-scale sub-architectures. An optimal combination of these sub-architectures can be implicitly learnt to best suit the target dataset(s). For diverse-scale feature extraction, a 3D extension of atrous spatial pyramid pooling (ASPP3D) is used in each fabric node for a fine-grained coverage of rich-scale image features. In the first experiment, FIRENet performed 3D universal bone segmentation of multiple musculoskeletal datasets of the human knee, shoulder and hip joints and exhibited excellent simultaneous multi-dataset segmentation performance. When tested for transfer learning, FIRENet further exhibited excellent single dataset performance (when pre-training on a prostate dataset), as well as significantly improved universal bone segmentation performance. The following experiment involves the simultaneous segmentation of the 10 Medical Segmentation Decathlon (MSD) challenge datasets. FIRENet demonstrated good multi-dataset segmentation results and inter-dataset adaptability of highly diverse image sizes. In both experiments, FIRENet's streamlined multi-dataset learning with one unified network that requires no hyper-parameter tuning.
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在医学图像分析中,许多疾病的微妙视觉特征要具有挑战性,尤其是由于缺乏配对数据。例如,在温和的阿尔茨海默氏病(AD)中,很难从纯成像数据中观察到脑组织萎缩,尤其是没有配对的AD和认知正常(CN)数据以进行比较。这项工作介绍了疾病发现甘(Didigan),这是一种基于弱的基于风格的框架,可发现和可视化细微的疾病特征。 Didigan了解了AD和CN视觉特征的疾病歧管,并将此歧管采样的样式代码施加到解剖结构“蓝图”上,以综合配对AD和CN磁共振图像(MRIS)。为了抑制生成的AD和CN对之间的非疾病相关变化,Didigan利用具有循环一致性和抗偏置的结构约束来实施解剖对应关系。当对阿尔茨海默氏病神经影像学计划(ADNI)数据集进行测试时,Didigan通过合成的配对AD和CN扫描显示了关键的AD特征(减少海马体积,心室增大和皮质结构的萎缩)。定性结果通过自动化的大脑体积分析来支持,其中还测量了脑组织结构的系统成对降低
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深度学习模型往往不是由于依赖虚假特征来解决任务的依赖而不是分布的。反事实数据增强提供了一种(大约)实现伪造特征反事实的表示形式的一般方法,这是对分布(OOD)鲁棒性的要求。在这项工作中,我们表明,如果增强功能是由{\ em上下文估计机器}执行的,则反事实数据扩展可能无法实现所需的反事实不变性。我们从理论上分析了这种反事实数据增强所施加的不变性,并描述了一个示例性NLP任务,在这种情况下,通过上下文猜测机器的反事实数据增强并不会导致强大的OOD分类器。
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广泛观察到的神经缩放定律,其中错误是训练集大小,模型大小或两者兼而有之的误差,从而促进了深度学习的实质性改进。但是,仅通过缩放来进行这些改进就需要计算和能源成本相当大。在这里,我们专注于数据集大小的错误缩放,并展示在理论和实践中如何超越幂律的扩展,并将其减少到指数缩放,如果我们可以访问高质量的数据修剪指标,以将顺序排名为应该丢弃哪些培训示例以实现任何修剪的数据集大小。然后,我们通过经验修剪的数据集大小来测试这一新的指数缩放预测,并且实际上观察到了在CIFAR-10,SVHN和Imagenet训练的重新NET上的功率定律缩放性能。鉴于找到高质量的修剪指标的重要性,我们对ImageNet上十个不同的数据修剪指标进行了第一个大规模的基准测试研究。我们发现大多数现有的高性能指标尺寸较差,而对于ImageNet来说,最佳尺度是计算密集型的,并且需要为每个图像标签。因此,我们开发了一种新的简单,便宜和可扩展的自我监督的修剪指标,该指标与最佳监督指标相当。总体而言,我们的工作表明,发现良好的数据指标可能会为可行的途径提供可行的途径,从而大大改善神经缩放法律,从而降低现代深度学习的资源成本。
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通过有限元(FE)模型对工程需求参数(EDP)的计算昂贵估计,同时考虑地震和参数不确定性限制了基于性能的地震工程框架的使用。已经尝试用替代模型代替FE模型,但是,这些模型中的大多数仅是构建参数的函数。这需要重新训练替代物以前未见地震。在本文中,作者提出了一个基于机器学习的替代模型框架,该框架考虑了这两种不确定性,以预测看不见的地震。因此,地震的特征在于使用代表性地面运动套件的SVD计算的正顺序基础。这使人们能够通过随机采样这些权重并将其乘以基础来产生大量的地震。权重以及本构参数作为用EDP作为所需输出的机器学习模型的输入。测试了四个竞争机器学习模型,并观察到一个深神经网络(DNN)给出了最准确的预测。该框架通过使用它成功预测了使用棒模型代表的一层楼和三层建筑的峰值响应来验证该框架,并受到看不见的远场地面运动。
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