bibtext: @InProceedings{ Lee_COVID19_MICCAI2024,
                   author = { Lee, Jong Bub and Kim, Jung Soo and Lee, Hyun Gyu },
                   title = { { COVID19 to Pneumonia: Multi Region Lung Severity Classification using CNN Transformer Position-Aware Feature Encoding Network } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
abstract: This study investigates utilizing chest X-ray (CXR) data from COVID-19 patients for classifying pneumonia severity, aiming to enhance prediction accuracy in COVID-19 datasets and achieve robust classification across diverse pneumonia cases. A novel CNN-Transformer hybrid network has been developed, leveraging position-aware features and Region Shared MLPs for integrating lung region information. This improves adaptability to different spatial resolutions and scores, addressing the subjectivity of severity assessment due to unclear clinical measurements. The model shows significant improvement in pneumonia severity classification for both COVID-19 and heterogeneous pneumonia datasets. Its adaptable structure allows seamless integration with various backbone models, leading to continuous performance improvement and potential clinical applications, particularly in intensive care units.
title: COVID19 to Pneumonia: Multi Region Lung Severity Classification using CNN Transformer Position-Aware Feature Encoding Network
authors: Lee, Jong Bub Kim, Jung Soo Lee, Hyun Gyu