  Automatic abnormality detection in abdominal CT scans can help doctors
improve the accuracy and efficiency in diagnosis. In this paper we aim at
detecting pancreatic ductal adenocarcinoma (PDAC), the most common pancreatic
cancer. Taking the fact that the existence of tumor can affect both the shape
and the texture of pancreas, we design a system to extract the shape and
texture feature at the same time for detecting PDAC. In this paper we propose a
two-stage method for this 3D classification task. First, we segment the
pancreas into a binary mask. Second, a FusionNet is proposed to take both the
binary mask and CT image as input and perform a binary classification. The
optimal architecture of the FusionNet is obtained by searching a pre-defined
functional space. We show that the classification results using either shape or
texture information are complementary, and by fusing them with the optimized
architecture, the performance improves by a large margin. Our method achieves a
specificity of 97% and a sensitivity of 92% on 200 normal scans and 136 scans
with PDAC.
