PDF(Pubmed)
Abstract：
Healthcare delivery during the initial days of outbreak of COVID-19 pandemic was badly impacted due to large number of severely infected patients posing an unprecedented global challenge. Although the importance of Chest X-rays (CXRs) in meeting this challenge has now been widely recognized, speedy diagnosis of CXRs remains an outstanding challenge because of fewer Radiologists. The exponential increase in Smart Phone ownership globally, including LMICs, provides an opportunity for exploring AI-driven diagnostic tools when provided with large volumes of CXRs transmitted through Smart Phones. However, the challenges associated with such systems have not been studied to the best of our knowledge. In this paper, we show that the predictions of AI-driven models on CXR images transmitted through Smart Phones via applications, such as WhatsApp, suffer both in terms of Predictability and Explainability, two key aspects of any automated Medical Diagnosis system. We find that several existing Deep learning based models exhibit prediction instability-disagreement between the prediction outcome of the original image and the transmitted image. Concomitantly we find that the explainability of the models deteriorate substantially, prediction on the transmitted CXR is often driven by features present outside the lung region, clearly a manifestation of Spurious Correlations. Our study reveals that there is significant compression of high-resolution CXR images, sometimes as high as 95%, and this could be the reason behind these two problems. Apart from demonstrating these problems, our main contribution is to show that Multi-Task learning (MTL) can serve as an effective bulwark against the aforementioned problems. We show that MTL models exhibit substantially more robustness, 40% over existing baselines. Explainability of such models, when measured by a saliency score dependent on out-of-lung features, also show a 35% improvement. The study is conducted on WaCXR dataset, a curated dataset of 6562 image pairs corresponding to original uncompressed and WhatsApp compressed CXR images. Keeping in mind that there are no previous datasets to study such problems, we open-source this data along with all implementations.
摘要：
由于大量严重感染的患者构成了前所未有的全球挑战，在COVID-19大流行爆发的最初几天，医疗保健服务受到了严重影响。尽管胸部X射线（CXR）在应对这一挑战中的重要性现已得到广泛认可，快速诊断CXRs仍然是一个突出的挑战，因为更少的放射科医生。全球智能手机拥有量呈指数级增长，包括LMICs，当提供通过智能手机传输的大量CXR时，为探索AI驱动的诊断工具提供了机会。然而,据我们所知，与此类系统相关的挑战尚未得到研究。在本文中,我们展示了AI驱动模型对通过智能手机通过应用程序传输的CXR图像的预测，比如WhatsApp,在可预测性和可解释性方面都受到影响，任何自动化医疗诊断系统的两个关键方面。我们发现，一些现有的基于深度学习的模型在原始图像和传输图像的预测结果之间表现出预测不稳定性-不一致。与此同时，我们发现模型的可解释性大幅下降，对传播的CXR的预测通常是由肺部区域之外的特征驱动的，显然是虚假关联的表现。我们的研究表明，高分辨率CXR图像有显著的压缩，有时高达95%，这可能是这两个问题背后的原因。除了证明这些问题，我们的主要贡献是表明多任务学习（MTL）可以作为解决上述问题的有效障碍。我们证明了MTL模型表现出更高的鲁棒性，比现有基线高出40%。这些模型的可解释性，当通过依赖于肺外特征的显著性评分来测量时，也显示出35%的改善。这项研究是在WaCXR数据集上进行的，与原始未压缩和WhatsApp压缩的CXR图像相对应的6562个图像对的精选数据集。请记住，以前没有数据集来研究这些问题，我们开源这些数据以及所有实现。
