The detection and characterization of lymph nodes through interpreting abdominal medical images are significant for diagnosing and treating colorectal cancer recurrence. However, interpreting abdominal medical images manually is labor-intensive and time-consuming. The related radiology education has many limitations as well. In this context, we seek to build an extensive collection of abdominal medical images with ground truth labels for lymph nodes recognition research and help junior doctors to train their interpretation skills. Therefore, we develop TeachMe, which is a web-based teaching system for annotating abdominal lymph nodes. The system has a three-level annotation-review workflow to construct an expert database of abdominal lymph nodes and a feedback mechanism helping junior doctors to learn the tricks of interpreting abdominal medical images. TeachMe's functionalities make itself stand out against other platforms. To validate these functionalities, we invite a medical team from Gastrointestinal Surgery Center, West China Hospital, to participate in the data collection workflow and experience the feedback mechanism. With the help of TeachMe, an expert dataset of abdominal lymph nodes has been created and an automated detection model for abdominal lymph nodes with incredible performances has been proposed. Moreover, through three rounds of practicing via TeachMe, our junior doctors' interpretation skills have been improved.
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