  The lethal nature of pancreatic ductal adenocarcinoma (PDAC) calls for early
differential diagnosis of pancreatic cysts, which are identified in up to 16%
of normal subjects, and some of which may develop into PDAC. Previous
computer-aided developments have achieved certain accuracy for classification
on segmented cystic lesions in CT. However, pancreatic cysts have a large
variation in size and shape, and the precise segmentation of them remains
rather challenging, which restricts the computer-aided interpretation of CT
images acquired for differential diagnosis. We propose a computer-aided
framework for early differential diagnosis of pancreatic cysts without
pre-segmenting the lesions using densely-connected convolutional networks
(Dense-Net). The Dense-Net learns high-level features from whole abnormal
pancreas and builds mappings between medical imaging appearance to different
pathological types of pancreatic cysts. To enhance the clinical applicability,
we integrate saliency maps in the framework to assist the physicians to
understand the decision of the deep learning method. The test on a cohort of
206 patients with 4 pathologically confirmed subtypes of pancreatic cysts has
achieved an overall accuracy of 72.8%, which is significantly higher than the
baseline accuracy of 48.1%, which strongly supports the clinical potential of
our developed method.
