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

点击翻译按钮获取中文摘要

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).