@Article{cmes.2023.030236,
AUTHOR = {Muwei Jian, Yue Jin, Hui Yu},
TITLE = {Enhanced Temporal Correlation for Universal Lesion Detection},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {138},
YEAR = {2024},
NUMBER = {3},
PAGES = {3051--3063},
URL = {http://www.techscience.com/CMES/v138n3/54932},
ISSN = {1526-1506},
ABSTRACT = {Universal lesion detection (ULD) methods for computed tomography (CT) images play a vital role in the modern clinical medicine and intelligent automation. It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks. However, 3D CT blocks necessitate significantly higher hardware resources during the learning phase. Therefore, efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks. In this paper, we propose a ULD network with the enhanced temporal correlation for this purpose, named TCE-Net. The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices. Besides, we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes. Extensive experiments are conducted on the DeepLesion benchmark demonstrate that this method achieves 66.84% and 78.18% for FS@0.5 and FS@1.0, respectively, outperforming compared state-of-the-art methods.},
DOI = {10.32604/cmes.2023.030236}
}