Title: Validation of a deep learning mammography model in a population with low screening rates Show Chinese title

Title: 在低筛查率人群中的深度学习乳腺X线摄影模型验证

Abstract: A key promise of AI applications in healthcare is in increasing access to quality medical care in under-served populations and emerging markets. However, deep learning models are often only trained on data from advantaged populations that have the infrastructure and resources required for large-scale data collection. In this paper, we aim to empirically investigate the potential impact of such biases on breast cancer detection in mammograms. We specifically explore how a deep learning algorithm trained on screening mammograms from the US and UK generalizes to mammograms collected at a hospital in China, where screening is not widely implemented. For the evaluation, we use a top-scoring model developed for the Digital Mammography DREAM Challenge. Despite the change in institution and population composition, we find that the model generalizes well, exhibiting similar performance to that achieved in the DREAM Challenge, even when controlling for tumor size. We also illustrate a simple but effective method for filtering predictions based on model variance, which can be particularly useful for deployment in new settings. While there are many components in developing a clinically effective system, these results represent a promising step towards increasing access to life-saving screening mammography in populations where screening rates are currently low. Show Chinese abstract

Abstract: 人工智能在医疗保健领域的应用的一个关键承诺是提高资源匮乏人群和新兴市场获取优质医疗服务的机会。然而，深度学习模型通常仅在拥有基础设施和资源进行大规模数据收集的优势人群中进行训练。在本文中，我们旨在实证研究这种偏差对乳腺癌在乳腺X线摄影中的检测潜力的影响。我们特别探讨了一个在北美和英国的筛查乳腺X线摄影数据上训练的深度学习算法，如何推广到中国一家医院的乳腺X线摄影数据，该医院尚未广泛实施筛查。在评估中，我们使用了为数字乳腺X线摄影DREAM挑战赛开发的顶级模型。尽管机构和人口构成发生了变化，我们发现该模型具有良好的泛化能力，在控制肿瘤大小的情况下，其表现与在DREAM挑战赛中取得的表现相似。我们还展示了一种简单但有效的基于模型方差过滤预测的方法，这在新环境部署时可能特别有用。虽然开发一个临床上有效的系统有许多组成部分，但这些结果代表了在提高当前筛查率较低的人群获取挽救生命的筛查乳腺X线摄影方面的一个有希望的步骤。