基于变压器的架构在许多下游流动任务中显示出显着的结果，包括问题应答。另一方面，数据的可用性阻碍了获得低资源语言的合法性能。在本文中，我们调查了预先训练的多语言模型的适用性，以提高低资源语言的问题的表现。我们使用与MLQA DataSet类似的七种语言进行多语言变压器架构测试了四种语言和任务适配器的组合。此外，我们还提出了使用语言和任务适配器回答的低资源问题的零拍摄转移学习。我们观察到堆叠语言和任务适配器对低资源语言的微语文变压器模型的性能显着提高。

在过去几年中，在医疗保健中使用人工智能（AI）已成为一个非常活跃的研究领域。虽然在图像分类任务中取得了重大进展，但实际上只能部署一些AI方法。目前积极使用临床AI模型的主要障碍是这些模型的可信度。这些复杂模型更常见，是一种黑色盒子，其中产生了有希望的结果。然而，当仔细检查时，这些模型开始在决策期间揭示隐式偏差，例如检测种族并对民族群体和群体具有偏见。在我们正在进行的研究中，我们开发了一个两步的逆势脱叠方法，部分学习可以减少种族差异，同时保留目标任务的性能。该方法已经在两个独立的医学图像案例研究 - 胸X射线和乳房X光检查中进行了评估，并在保持目标性能的同时表现出偏差减少的承诺。

Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models. 1