RT Journal Article
T1 Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels
A1 Li, Ming
A1 Fang, Yingying
A1 Tang, Zeyu
A1 Onuorah, Chibudom
A1 Xia, Jun
A1 Ser, Javier Del
A1 Walsh, Simon
A1 Yang, Guang
AB The upheaval brought by the arrival of the COVID-19 pandemic has continued to bring fresh challenges over the past two years. During this COVID-19 pandemic, there has been a need for rapid identification of infected patients and specific delineation of infection areas in computed tomography (CT) images. Although deep supervised learning methods have been established quickly, the scarcity of both image-level and pixel-level labels as well as the lack of explainable transparency still hinder the applicability of AI. Can we identify infected patients and delineate the infections with extreme minimal supervision? Semi-supervised learning has demonstrated promising performance under limited labelled data and sufficient unlabelled data. Inspired by semi-supervised learning, we propose a model-agnostic calibrated pseudo-labelling strategy and apply it under a consistency regularization framework to generate explainable identification and delineation results. We demonstrate the effectiveness of our model with the combination of limited labelled data and sufficient unlabelled data or weakly-labelled data. Extensive experiments have shown that our model can efficiently utilize limited labelled data and provide explainable classification and segmentation results for decision-making in clinical routine.
SN 2471-285X
YR 2023
FD 2023-02-01
LK https://hdl.handle.net/11556/5087
UL https://hdl.handle.net/11556/5087
LA eng
NO Li , M , Fang , Y , Tang , Z , Onuorah , C , Xia , J , Ser , J D , Walsh , S & Yang , G 2023 , ' Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels ' , IEEE Transactions on Emerging Topics in Computational Intelligence , vol. 7 , no. 1 , pp. 26-35 . https://doi.org/10.1109/TETCI.2022.3189054
NO Publisher Copyright: © 2017 IEEE.
DS TECNALIA Publications
RD 30 sept 2024