Recent advances in deep learning have transformed computer-assisted intervention and surgical video analysis, driving improvements not only in surgical training, intraoperative decision support, and patient outcomes, but also in postoperative documentation and surgical discovery. Central to these developments is the availability of large, high-quality annotated datasets. In gynecologic laparoscopy, surgical scene understanding and action recognition are fundamental for building intelligent systems that assist surgeons during operations and provide deeper analysis after surgery. However, existing datasets are often limited by small scale, narrow task focus, or insufficiently detailed annotations, limiting their utility for comprehensive, end-to-end workflow analysis. To address these limitations, we introduce GynSurg, the largest and most diverse multi-task dataset for gynecologic laparoscopic surgery to date. GynSurg provides rich annotations across multiple tasks, supporting applications in action recognition, semantic segmentation, surgical documentation, and discovery of novel procedural insights. We demonstrate the dataset's quality and versatility by benchmarking state-of-the-art models under a standardized training protocol. To accelerate progress in the field, we publicly release the GynSurg dataset and its annotations (https://ftp.itec.aau.at/datasets/GynSurge/).

In this paper, we consider the recently proposed family of continual learning models, called Gated Linear Networks (GLNs), and study two crucial aspects impacting on the amount of catastrophic forgetting affecting gated linear networks, namely, data standardization and gating mechanism. Data standardization is particularly challenging in the online/continual learning setting because data from future tasks is not available beforehand. The results obtained using an online standardization method show a considerably higher amount of forgetting compared to an offline -static- standardization. Interestingly, with the latter standardization, we observe that GLNs show almost no forgetting on the considered benchmark datasets. Secondly, for an effective GLNs, it is essential to tailor the hyperparameters of the gating mechanism to the data distribution. In this paper, we propose a gating strategy based on a set of prototypes and the resulting Voronoi tessellation. The experimental assessment shows that the proposed approach is more robust to different data standardizations compared to the original one, based on a halfspace gating mechanism, and shows improved predictive performance.