In various fields of data science, researchers are often interested in estimating the ratio of conditional expectation functions (CEFR). Specifically in causal inference problems, it is sometimes natural to consider ratio-based treatment effects, such as odds ratios and hazard ratios, and even difference-based treatment effects are identified as CEFR in some empirically relevant settings. This chapter develops the general framework for estimation and inference on CEFR, which allows the use of flexible machine learning for infinite-dimensional nuisance parameters. In the first stage of the framework, the orthogonal signals are constructed using debiased machine learning techniques to mitigate the negative impacts of the regularization bias in the nuisance estimates on the target estimates. The signals are then combined with a novel series estimator tailored for CEFR. We derive the pointwise and uniform asymptotic results for estimation and inference on CEFR, including the validity of the Gaussian bootstrap, and provide low-level sufficient conditions to apply the proposed framework to some specific examples. We demonstrate the finite-sample performance of the series estimator constructed under the proposed framework by numerical simulations. Finally, we apply the proposed method to estimate the causal effect of the 401(k) program on household assets.

在社会科学和企业中观测数据的分析中，难以获得“（准）单源数据集”，其中同时观察到感兴趣的变量。相反，通常针对不同的个体或单位获取多源数据集。已经提出了各种方法来研究每个数据集中的变量之间的关系，例如匹配和潜在的变量建模。有必要利用这些数据集作为具有缺失变量的单源数据集。现有方法假设要集成的数据集是从相同的人群中获取，或者采样取决于协变量。在缺失方面，这种假设被称为随机（MAR）缺失。然而，正如在应用研究中所示的那样，这一假设可能不会在实际数据分析中保持，并且获得的结果可能偏置。我们提出了一种数据融合方法，不认为数据集是均匀的。我们使用用于非MAR缺失数据的高斯过程潜变量模型。该模型假设关注的变量和缺失的概率取决于潜在变量。模拟研究和实际数据分析表明，具有缺失数据机制和潜在高斯过程的提出方法产生有效估计，而现有方法提供严重偏置的估计。这是第一研究，其中在数据融合问题中的可谐振假设下考虑并解决了对数据集的非随机分配。

Background and objective: COVID-19 and its variants have caused significant disruptions in over 200 countries and regions worldwide, affecting the health and lives of billions of people. Detecting COVID-19 from chest X-Ray (CXR) images has become one of the fastest and easiest methods for detecting COVID-19 since the common occurrence of radiological pneumonia findings in COVID-19 patients. We present a novel high-accuracy COVID-19 detection method that uses CXR images. Methods: Our method consists of two phases. One is self-supervised learning-based pertaining; the other is batch knowledge ensembling-based fine-tuning. Self-supervised learning-based pretraining can learn distinguished representations from CXR images without manually annotated labels. On the other hand, batch knowledge ensembling-based fine-tuning can utilize category knowledge of images in a batch according to their visual feature similarities to improve detection performance. Unlike our previous implementation, we introduce batch knowledge ensembling into the fine-tuning phase, reducing the memory used in self-supervised learning and improving COVID-19 detection accuracy. Results: On two public COVID-19 CXR datasets, namely, a large dataset and an unbalanced dataset, our method exhibited promising COVID-19 detection performance. Our method maintains high detection accuracy even when annotated CXR training images are reduced significantly (e.g., using only 10% of the original dataset). In addition, our method is insensitive to changes in hyperparameters. Conclusions: The proposed method outperforms other state-of-the-art COVID-19 detection methods in different settings. Our method can reduce the workloads of healthcare providers and radiologists.

Purpose: Considering several patients screened due to COVID-19 pandemic, computer-aided detection has strong potential in assisting clinical workflow efficiency and reducing the incidence of infections among radiologists and healthcare providers. Since many confirmed COVID-19 cases present radiological findings of pneumonia, radiologic examinations can be useful for fast detection. Therefore, chest radiography can be used to fast screen COVID-19 during the patient triage, thereby determining the priority of patient's care to help saturated medical facilities in a pandemic situation. Methods: In this paper, we propose a new learning scheme called self-supervised transfer learning for detecting COVID-19 from chest X-ray (CXR) images. We compared six self-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR, PIRL-jigsaw, and PIRL-rotation) with the proposed method. Additionally, we compared six pretrained DCNNs (ResNet18, ResNet50, ResNet101, CheXNet, DenseNet201, and InceptionV3) with the proposed method. We provide quantitative evaluation on the largest open COVID-19 CXR dataset and qualitative results for visual inspection. Results: Our method achieved a harmonic mean (HM) score of 0.985, AUC of 0.999, and four-class accuracy of 0.953. We also used the visualization technique Grad-CAM++ to generate visual explanations of different classes of CXR images with the proposed method to increase the interpretability. Conclusions: Our method shows that the knowledge learned from natural images using transfer learning is beneficial for SSL of the CXR images and boosts the performance of representation learning for COVID-19 detection. Our method promises to reduce the incidence of infections among radiologists and healthcare providers.

This paper solves a generalized version of the problem of multi-source model adaptation for semantic segmentation. Model adaptation is proposed as a new domain adaptation problem which requires access to a pre-trained model instead of data for the source domain. A general multi-source setting of model adaptation assumes strictly that each source domain shares a common label space with the target domain. As a relaxation, we allow the label space of each source domain to be a subset of that of the target domain and require the union of the source-domain label spaces to be equal to the target-domain label space. For the new setting named union-set multi-source model adaptation, we propose a method with a novel learning strategy named model-invariant feature learning, which takes full advantage of the diverse characteristics of the source-domain models, thereby improving the generalization in the target domain. We conduct extensive experiments in various adaptation settings to show the superiority of our method. The code is available at https://github.com/lzy7976/union-set-model-adaptation.

数据集复杂性评估旨在在训练分类器之前先预测具有复杂性计算的数据集上的分类性能，该分类器也可以用于分类器选择和减少数据集。深卷积神经网络（DCNN）的训练过程是迭代的且耗时的，这是由于高参数的不确定性和不同数据集引入的域移位。因此，通过在培训DCNN模型之前有效评估数据集的复杂性来预测分类性能是有意义的。本文提出了一种新的方法，称为Laplacian Spectrum（CMSAUL）下的累积最大缩放区域，该方法可以在六个数据集上实现最新的复杂性评估性能。

背景和目标：需要分享医疗数据以实现医疗保健信息的跨机构流量并构建高准确的计算机辅助诊断系统。但是，大量的医疗数据集，保存深度卷积神经网络（DCNN）模型的大量记忆以及患者的隐私保护是可能导致医疗数据共享效率低下的问题。因此，本研究提出了一种新型的软标签数据集蒸馏方法，用于医疗数据共享。方法：所提出的方法提炼医疗图像数据的有效信息，并生成几个带有不同数据分布的压缩图像，以供匿名医疗数据共享。此外，我们的方法可以提取DCNN模型的基本权重，以减少保存训练有素的模型以进行有效的医疗数据共享所需的内存。结果：所提出的方法可以将数万张图像压缩为几个软标签图像，并将受过训练的模型的大小减少到其原始大小的几百分之一。蒸馏后获得的压缩图像已在视觉上匿名化；因此，它们不包含患者的私人信息。此外，我们可以通过少量压缩图像实现高检测性能。结论：实验结果表明，所提出的方法可以提高医疗数据共享的效率和安全性。

高级模型的采集取决于许多领域的大型数据集，这使存储数据集和培训模型昂贵。作为解决方案，数据集蒸馏可以合成一个小数据集，以便在其上训练有素的模型在与原始大型数据集的情况下达到高性能。通过匹配网络参数的最近提出的数据集蒸馏方法已被证明对多个数据集有效。但是，蒸馏过程中的一些参数很难匹配，这会损害蒸馏性能。基于此观察结果，本文提出了一种使用参数修剪来解决问题的新方法。提出的方法可以通过在蒸馏过程中修剪难以匹配的参数来合成更强大的蒸馏数据集并改善蒸馏性能。三个数据集的实验结果表明，所提出的方法的表现优于其他SOTA数据集蒸馏方法。

由于存在隐私保护问题以及传输和存储许多高分辨率医疗图像的巨大成本，因此在医院之间共享医疗数据集很具有挑战性。但是，数据集蒸馏可以合成一个小数据集，从而使对其进行训练的模型与原始大型数据集实现了可比的性能，这显示了解决现有的医疗共享问题的潜力。因此，本文提出了一种基于数据集蒸馏的新型医学数据集共享方法。Covid-19胸部X射线图像数据集的实验结果表明，即使使用稀缺的匿名胸部X射线图像，我们的方法也可以达到高检测性能。

在没有监督信号的情况下学习简洁的数据表示是机器学习的基本挑战。实现此目标的一种突出方法是基于可能性的模型，例如变异自动编码器（VAE），以基于元元素来学习潜在表示，这是对下游任务有益的一般前提（例如，disentanglement）。但是，这种方法通常偏离原始的可能性体系结构，以应用引入的元优势，从而导致他们的培训不良变化。在本文中，我们提出了一种新颖的表示学习方法，Gromov-Wasserstein自动编码器（GWAE），该方法与潜在和数据分布直接匹配。 GWAE模型不是基于可能性的目标，而是通过最小化Gromov-Wasserstein（GW）度量的训练优化。 GW度量测量了在无与伦比的空间上支持的分布之间的面向结构的差异，例如具有不同的维度。通过限制可训练的先验的家庭，我们可以介绍元主题来控制下游任务的潜在表示。与现有基于VAE的方法的经验比较表明，GWAE模型可以通过更改先前的家族而无需进一步修改GW目标来基于元家庭学习表示。