Generative data augmentation for biliary tract detection on intraoperative images

Due to their robustness and ability to effectively differentiate between target and background, discriminative approaches have become the leading techniques in object identification and tracking. The computational capabilities of GPUs allowed the transition to DL-based detection, leading to significant breakthroughs in object detection, improving the overall performances [19] [20]. Object detection algorithms commonly used in medicine leverage CNNs for their ability to automatically extract features from extensive training datasets, enabling accurate predictions. This has led to the development of various CNN-based methods in recent years for identifying and tracking laparoscopic instruments and organs, especially during Minimally Invasive Surgery (MIS) [21]. As reported in literature [22], Deep Convolutional Neural Network (DCNN)-based detectors can be categorized as either "two-stage" or "one-stage". Two-stage detectors separate object localization (generating region proposals) from object classification, whilst one-stage detectors perform both detection and classification simultaneously using DCNNs.