XAI-CBIR: Explainable AI System for Content based Retrieval of Video Frames from Minimally Invasive Surgery Videos

In this paper, we present a human-in-the-loop explainable AI (XAI) system for content based image retrieval (CBIR) of video frames similar to a query image from minimally invasive surgery (MIS) videos for surgical education. It extracts semantic descriptors from MIS video frames using a self-supervised deep learning model. It then employs an iterative query refinement strategy where in a binary classifier trained online based on relevance feedback from the user is used to iteratively refine the search results. Lastly, it uses an XAI technique to generate a saliency map that provides a visual explanation of why the system considers a retrieved image to be similar to the query image. We evaluated the proposed XAI-CBIR system on the public Cholec80 dataset containing 80 videos of minimally invasive cholecystectomy surgeries with encouraging results.