Enhanced temporal correlation for universal lesion detection

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 [email protected] and [email protected], respectively, outperforming compared state-of-the-art methods.