Leveraging Multiple CNNs for Triaging Medical Workflow
This addresses the need for faster triaging of critical cases in healthcare, but it is incremental as it builds on existing CNNs with a new rating system.
The paper tackled the problem of improving medical triaging workflows during Covid-19 by developing a system that uses multiple VGG16 CNNs to assign a critical index (0-10) to images, reordering batches from most to least critical roughly accurately.
High hospitalization rates due to the global spread of Covid-19 bring about a need for improvements to classical triaging workflows. To this end, convolutional neural networks (CNNs) can effectively differentiate critical from non-critical images so that critical cases may be addressed quickly, so long as there exists some representative image for the illness. Presented is a conglomerate neural network system consisting of multiple VGG16 CNNs; the system trains on weighted skin disease images re-labelled as critical or non-critical, to then attach to input images a critical index between 0 and 10. A critical index offers a more comprehensive rating system compared to binary critical/non-critical labels. Results for batches of input images run through the trained network are promising. A batch is shown being re-ordered by the proposed architecture from most critical to least critical roughly accurately.