论文标题
部分可观测时空混沌系统的无模型预测
Tutorial on the development of AI models for medical image analysis
论文作者
论文摘要
最早在1966年引入了使用计算机阅读医疗扫描的想法。但是，机器学习技术的限制意味着最初进展缓慢。 2012年的Alexnet突破引发了对该主题的新兴趣，这导致市场上发布了100秒的医疗AI解决方案。尽管对于某些疾病和方式取得了成功，但仍有许多挑战。研究通常着重于通过调查或挑战对临床研究或技术对特定应用或技术的开发，临床评估或元分析。但是，对改善现实世界绩效的发展过程的关注有限。在本教程中，我们讨论了后者，并讨论了进行开发过程的一些技术，以使其尽可能高效。
The idea of using computers to read medical scans was introduced as early as 1966. However, limits to machine learning technology meant progress was slow initially. The Alexnet breakthrough in 2012 sparked new interest in the topic, which resulted in the release of 100s of medical AI solutions on the market. In spite of success for some diseases and modalities, many challenges remain. Research typically focuses on the development of specific applications or techniques, clinical evaluation, or meta analysis of clinical studies or techniques through surveys or challenges. However, limited attention has been given to the development process of improving real world performance. In this tutorial, we address the latter and discuss some techniques to conduct the development process in order to make this as efficient as possible.
