Adaptation to CT Reconstruction Kernels by Enforcing Cross-Domain Feature Maps Consistency.

ABSTRACT: Deep learning methods provide significant assistance in analyzing coronavirus disease (COVID-19) in chest computed tomography (CT) images, including identification, severity assessment, and segmentation. Although the earlier developed methods address the lack of data and specific annotations, the current goal is to build a robust algorithm for clinical use, having a larger pool of available data. With the larger datasets, the domain shift problem arises, affecting the performance of methods on the unseen data. One of the critical sources of domain shift in CT images is the difference in reconstruction kernels used to generate images from the raw data (sinograms). In this paper, we show a decrease in the COVID-19 segmentation quality of the model trained on the smooth and tested on the shar

SUBMITTER: Shimovolos S 

PROVIDER: S-EPMC9503667 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature