Automatic Annotation of Medical Images for AI Model Training, Xiaoqin Wang, MD
Artificial Intelligence (AI), especially deep learning, has demonstrated revolutionary ability in various medical imaging analysis tasks. However, AI application in medical imaging is limited by the lack of annotated image data. Imaging annotation by experts is expensive and a new tool is needed for automatic image annotation. In this work, we propose a novel, weakly-supervised, self training computer network for breast cancer annotation on mammograms. Our results show our model has improved performance compared to other approaches trained similarily. This new tool can potentially reduce the annotation burden on human experts and can accelerate AI research in medical imaging.
Examples for AI detection of breast cancer on mammograms. Red boxes are bounding boxes for tumor location provided automatically by NLP interpretation of Radiological report and unavailable to AI algorithm. The heatmaps show the AI-predicted location of tumor corresponding closely to the Radiologist's label.