Figure 1
From: Video-based formative and summative assessment of surgical tasks using deep learning
Overview of the study. (a) Subject demographics and descriptive data. (b) The pipeline of the VBA-Net. The model utilizes Mask R-CNN to generate tool motion sequences from video frames. Then denoising autoencoder (DAE) embeds the sequences for the classifier to predict summative and formative performance. The primary PC dataset is used to develop the model, i.e., tune its hyperparameters. The additional PC dataset, on the other hand, is used for validation. The JIGSAWS dataset is utilized to benchmark the model against the high-performing models in the literature.
