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

The majority of the encouraging experimental results published on AI-based endoscopic Computer-Aided Detection (CAD) systems have not yet been reproduced in clinical settings, mainly due to highly curated datasets used throughout the experimental phase of the research. In a realistic clinical environment, these necessary high image-quality standards cannot be guaranteed, and the CAD system performance may degrade. While several studies have previously presented impressive outcomes with Frame Informativeness Assessment (FIA) algorithms, the current-state of the art implies sequential use of FIA and CAD systems, affecting the time performance of both algorithms. Since these algorithms are often trained on similar datasets, we hypothesise that part of the learned feature representations can be leveraged for both systems, enabling a more efficient implementation. This paper explores this case for early Barrett cancer detection by integrating the FIA algorithm within the CAD system. Sharing the weights between two tasks reduces the number of parameters from 16 to 11 million and the number of floating-point operations from 502 to 452 million. Due to the lower complexity of the architecture, the proposed model leads to inference time up to 2 times faster than the state-of-the-art sequential implementation while retaining the classification performance.