Rethinking Feature Extraction: Gradient-Based Localized Feature Extraction for End-To-End Surgical Downstream Tasks

Several approaches have been introduced to understand surgical scenes through downstream tasks like captioning and surgical scene graph generation. However, most of them heavily rely on an independent object detector and region-based feature extractor. Encompassing computationally expensive detection and feature extraction models, these multi-stage methods suffer from slow inference speed, making them less suitable for real-time surgical applications. The performance of the downstream tasks also degrades from inheriting errors of the earlier modules of the pipeline. This work develops a detector-free gradient-based localized feature extraction approach that enables end-to-end model training for downstream surgical tasks such as report generation and tool-tissue interaction graph prediction. We eliminate the need for object detection or region proposal and feature extraction networks by extracting the features of interest from the discriminative regions in the feature map of the classification models. Here, the discriminative regions are localized using gradient-based localization techniques (e.g. Grad-CAM). We show that our proposed approaches enable the real-time deployment of end-to-end models for surgical downstream tasks. We extensively validate our approach on two surgical tasks: captioning and scene graph generation. The results prove that our gradient-based localized feature extraction methods effectively substitute the detector and feature extractor networks, allowing end-to-end model development with faster inference speed, essential for real-time surgical scene understanding tasks.