  Multi-phase computed tomography (CT) has been widely used for the
preoperative diagnosis of kidney cancer due to its non-invasive nature and
ability to characterize renal lesions. However, since enhancement patterns of
renal lesions across CT phases are different even for the same lesion type, the
visual assessment by radiologists suffers from inter-observer variability in
clinical practice. Although deep learning-based approaches have been recently
explored for differential diagnosis of kidney cancer, they do not explicitly
model the relationships between CT phases in the network design, limiting the
diagnostic performance. In this paper, we propose a novel lesion-aware
cross-phase attention network (LACPANet) that can effectively capture temporal
dependencies of renal lesions across CT phases to accurately classify the
lesions into five major pathological subtypes from time-series multi-phase CT
images. We introduce a 3D inter-phase lesion-aware attention mechanism to learn
effective 3D lesion features that are used to estimate attention weights
describing the inter-phase relations of the enhancement patterns. We also
present a multi-scale attention scheme to capture and aggregate temporal
patterns of lesion features at different spatial scales for further
improvement. Extensive experiments on multi-phase CT scans of kidney cancer
patients from the collected dataset demonstrate that our LACPANet outperforms
state-of-the-art approaches in diagnostic accuracy.
