论文标题
基于COVID-19
Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies
论文作者
论文摘要
Covid-19疾病的突然爆发和不受控制的传播是当今最重要的全球问题之一。在短时间内，这导致了许多深层神经网络模型的开发，用于使用模块进行解释。在这项工作中，我们对所提出模型的各个方面进行了系统分析。我们的分析表明，在数据获取，模型开发和解释构建的不同阶段犯了许多错误。在这项工作中，我们概述了被调查的机器学习文章中提出的方法，并表明缺乏对射线照相领域的深刻理解而产生的典型错误。我们介绍了这两者的观点：该领域的专家 - 放射科学家和深度学习工程师处理模型解释。最终结果是提出的清单，其最小条件将通过可靠的Covid-19诊断模型满足。
The sudden outbreak and uncontrolled spread of COVID-19 disease is one of the most important global problems today. In a short period of time, it has led to the development of many deep neural network models for COVID-19 detection with modules for explainability. In this work, we carry out a systematic analysis of various aspects of proposed models. Our analysis revealed numerous mistakes made at different stages of data acquisition, model development, and explanation construction. In this work, we overview the approaches proposed in the surveyed Machine Learning articles and indicate typical errors emerging from the lack of deep understanding of the radiography domain. We present the perspective of both: experts in the field - radiologists and deep learning engineers dealing with model explanations. The final result is a proposed checklist with the minimum conditions to be met by a reliable COVID-19 diagnostic model.
