论文标题
一项有关从医学图像中自动报告生成的深度学习和解释性的调查
A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images
论文作者
论文摘要
每年，医生面临对患者对基于图像的诊断的需求，这一问题可以通过最近的人工智能方法解决。在这种情况下，我们调查在医学图像中自动报告生成领域，重点是使用深神经网络的方法，相对于：（1）数据集，（2）体系结构设计，（3）解释性和（4）评估指标。我们的调查确定了有趣的发展，但仍存在挑战。其中，当前对生成报告的评估特别薄弱，因为它主要依赖于传统的自然语言处理（NLP）指标，这些指标不能准确地捕获医疗正确性。
Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods. In this context, we survey works in the area of automatic report generation from medical images, with emphasis on methods using deep neural networks, with respect to: (1) Datasets, (2) Architecture Design, (3) Explainability and (4) Evaluation Metrics. Our survey identifies interesting developments, but also remaining challenges. Among them, the current evaluation of generated reports is especially weak, since it mostly relies on traditional Natural Language Processing (NLP) metrics, which do not accurately capture medical correctness.
