Non-relevant segment recognition via hard example mining under sparsely distributed events

We propose on/offline hard example mining (HEM) techniques to alleviate the degradation of the generalization performance in the sparse distribution of events in non-relevant segment (NRS) recognition and to examine their utility for long-duration surgery. Through on/offline HEM, higher recognition performance can be achieved by extracting hard examples that help train NRS events, for a given training dataset. Furthermore, we provide two performance measurement metrics to quantitatively evaluate NRS recognition in the clinical field. The existing precision and recall-based performance measurement method provides accurate quantitative statistics. However, it is not an efficient evaluation metric in tasks where false positive recognition errors are fatal, such as NRS recognition. We measured the false discovery rate (FDR) and threat score (TS) to provide quantitative values that meet the needs of the clinical setting. Finally, unlike previous studies, the utility of NRS recognition was improved by applying our model to long-duration surgeries, instead of short-length surgical operations such as cholecystectomy. In addition, the proposed training methodology was applied to robotic and laparoscopic surgery datasets to verify that it can be robustly applied to various clinical environments.