Efficient endoscopic frame informativeness assessment by reusing the encoder of the primary CAD task

The majority of encouraging results published for endoscopic Computer-Aided Detection (CAD) algorithms employ datasets adhering to high-quality standards that cannot be guaranteed at peripheral medical centres. Several Frame Informativeness Assessment (FIA) systems have been proposed in the literature to address low-quality endoscopic frames. However, current state-of-the-art implies sequential use of FIA and CAD, affecting the time performance of both algorithms. Since these algorithms process similar images, we hypothesize that part of the learned features can be leveraged for both systems, enabling optimized implementation. We explore this case for early Barrett cancer detection by integrating the FIA within the CAD system.