Integration of Human and Machine Observers for the Interpretation of Medical Images

医学图像必须由医学专业人员进行解释，以确定是否存在疾病。像全人类一样，医学专业人士的注意力有限，也会犯错。此外，现代成像设备产生更大、更复杂的数据，人类观察者难以分析这些数据。现代计算机算法可以识别更细微的模式，可以分析更大的数据集，可以从更多的例子中学习，并且是可重复的。然而，我们并不总是知道如何为计算机编程来完成某些任务，因此依赖自适应算法来“学习”检测疾病。为了利用专家人类观察者和机器观察者的力量，我们需要人和机器之间的双向合作。在这个项目中，我们建议与其供应商(Hermes Medical Solutions)合作，调整现有的医学图像查看系统，以实现计算机辅助诊断(机器帮助医生)和有监督的机器学习(用户向学习机提供纠正反馈)。

Medical images must be interpreted by medical professionals to determine the presence of disease. Like allhumans, medical professionals have limited attention and make mistakes. Furthermore, modern imagingequipment produce larger and more complex data which the human observer struggles to analyze. Moderncomputer algorithms can identify more subtle patterns, can analyze larger datasets, learn from more examplesand are reproducible. Nevertheless we do not always know how to program computers for certain tasks andtherefore rely on adaptive algorithms to "learn" to detect disease. To leverage the strength of expert humanobservers and machine observers we require two-way collaboration between man and machine. In this projectwe propose to adapt an existing medical image viewing system, in collaboration with its vendor (HermesMedical Solutions), to enable computer aided diagnosis (where the machine helps the physician) andsupervised machine learning (where the user provides corrective feedback to the learning machine).