AI-human interactive pipeline with feedback to accelerate medical image annotation
Youngwon Choi, Marlena Garcia, Steven S. Raman, Dieter R. Enzmann, Matthew S. Brown · Medical Imaging 2022: Computer-Aided Diagnosis · 2022
We propose an AI-human interactive pipeline to accelerate medical image annotation of large data sets. This pipeline continuously iterates on three steps. First, an AI system provides initial automated annotations to image analysts. Second, the analysts edit the annotations. Third, the AI system is upgraded with analysts’ feedback, thus enabling more efficient annotation. To develop this pipeline, we propose an AI system and upgraded workflow that is focused on reducing the annotation time while maintaining accuracy. We demonstrated the ability of the feedback loop to accelerate the task of prostate MRI segmentation. With the initial iterations on small batch sizes, the annotation time was reduced substantially.