论文标题
用自然语言解释胸部X射线病理
Explaining Chest X-ray Pathologies in Natural Language
论文作者
论文摘要
大多数深度学习算法都缺乏对其预测的解释，这限制了其在临床实践中的部署。改善解释性的方法，尤其是在医学成像中，经常被证明可以传达有限的信息，过于放心或缺乏鲁棒性。在这项工作中，我们介绍了生成自然语言解释（NLE）的任务，以证明对医学图像的预测是合理的。 NLE是人类友好且全面的，并能够培训可解释的模型。为了实现这一目标，我们介绍了Mimic-nle，这是第一个带有NLE的大型医学成像数据集。它包含超过38,000个NLE，这些NLE解释了各种胸部病理和胸部X射线发现的存在。我们提出了一种解决任务并评估该数据集上的几个架构的一般方法，包括通过临床医生评估。
Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice. Approaches to improve explainability, especially in medical imaging, have often been shown to convey limited information, be overly reassuring, or lack robustness. In this work, we introduce the task of generating natural language explanations (NLEs) to justify predictions made on medical images. NLEs are human-friendly and comprehensive, and enable the training of intrinsically explainable models. To this goal, we introduce MIMIC-NLE, the first, large-scale, medical imaging dataset with NLEs. It contains over 38,000 NLEs, which explain the presence of various thoracic pathologies and chest X-ray findings. We propose a general approach to solve the task and evaluate several architectures on this dataset, including via clinician assessment.
