Inter-slice correlation weighted fusion for universal lesion detection

Universal lesion detection using computerised tomography (CT) scans is a critical computer-aided diagnosis measure in clinical diagnosis. One of the key issues during the diagnosis is to identify the correlations between sequential slices to improve the feature representation of CT scans. In the process of fusing slice features containing temporal correlations, the correlation between the contextual slices in the channel dimension and the target slices is closely related to the spatial distance in practice. However, convolutional fusion approaches commonly ignore that features of different distances have unequal weights. To tackle this issue, we present a temporal correlation weighted fusion lesion detection network, called TCW-Net. Specifically, for the slices in the channel dimension, we develop a weighted feature fusion module to adjust the more discriminative features using learned weights. Then, we adapt a spatial offset attention mechanism that allows the detection network to pay more attention to the lesion's slight spatial offset and thus improve the model's capacity for distinguishing between different lesion features. Extensive experiments carried out on the DeepLesion dataset show that the proposed algorithm has superior performance over the state-of-the-art methods.