DeepOPG: Improving Orthopantomogram Finding Summarization with Weak Supervision
This work addresses a novel problem in dental imaging for improving patient communication and clinical efficiency, but it is incremental as it builds on existing segmentation and localization techniques.
The paper tackled the problem of summarizing clinical findings from orthopantomograms (dental panoramic radiographs) by developing DeepOPG, which achieved an overall AUC of 88.2% in detecting six types of findings and improved detection by adding 5.9% and 0.4% to AP@IoU=0.5 through novel modules.
Clinical finding summaries from an orthopantomogram, or a dental panoramic radiograph, have significant potential to improve patient communication and speed up clinical judgments. While orthopantomogram is a first-line tool for dental examinations, no existing work has explored the summarization of findings from it. A finding summary has to find teeth in the imaging study and label the teeth with several types of past treatments. To tackle the problem, we developDeepOPG that breaks the summarization process into functional segmentation and tooth localization, the latter of which is further refined by a novel dental coherence module. We also leverage weak supervision labels to improve detection results in a reinforcement learning scenario. Experiments show high efficacy of DeepOPG on finding summarization, achieving an overall AUC of 88.2% in detecting six types of findings. The proposed dental coherence and weak supervision both are shown to improve DeepOPG by adding 5.9% and 0.4% to AP@IoU=0.5.