Leveraging Auxiliary Information from EMR for Weakly Supervised Pulmonary Nodule Detection

Pulmonary nodule detection from lung computed tomography (CT) scans has been an active clinical research direction, benefiting the early diagnosis of lung cancer related disease. However, state-of-the-art deep learning models require instance-level annotation for the training data (i.e., a bounding box for each nodule), which require expensive costs and might not always be applicable. On the other hand, during clinical diagnosis of lung nodule detection, radiologists provide electronic medical records (EMR), which contain information such as the malignancy, number, texture of the detected nodules, and slice indices at which the nodules are located. Thus, the goal of this work is to utilize EMR information for learning pulmonary nodule detection models, without observing any nodule annotation during the training stage. To realize the above weakly supervised learning strategy, we extend multiple instance learning (MIL) and specifically take the presence and number of nodules in each CT scan, as well as the associated slice information, in our proposed deep learning framework. In our experiments, we present proper evaluation metrics for assessing and comparing the effectiveness of state-of-the-art models on multiple datasets, which verify the practicality of our proposed model.