  Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers
among the population. Screening for PDACs in dynamic contrast-enhanced CT is
beneficial for early diagnosis. In this paper, we investigate the problem of
automated detecting PDACs in multi-phase (arterial and venous) CT scans.
Multiple phases provide more information than single phase, but they are
unaligned and inhomogeneous in texture, making it difficult to combine
cross-phase information seamlessly. We study multiple phase alignment
strategies, i.e., early alignment (image registration), late alignment
(high-level feature registration), and slow alignment (multi-level feature
registration), and suggest an ensemble of all these alignments as a promising
way to boost the performance of PDAC detection. We provide an extensive
empirical evaluation on two PDAC datasets and show that the proposed alignment
ensemble significantly outperforms previous state-of-the-art approaches,
illustrating the strong potential for clinical use.
