  We aim to detect pancreatic ductal adenocarcinoma (PDAC) in abdominal CT
scans, which sheds light on early diagnosis of pancreatic cancer. This is a 3D
volume classification task with little training data. We propose a two-stage
framework, which first segments the pancreas into a binary mask, then
compresses the mask into a shape vector and performs abnormality
classification. Shape representation and classification are performed in a
joint manner, both to exploit the knowledge that PDAC often changes the shape
of the pancreas and to prevent over-fitting. Experiments are performed on 300
normal scans and 136 PDAC cases. We achieve a specificity of 90.2% (false alarm
occurs on less than 1/10 normal cases) at a sensitivity of 80.2% (less than 1/5
PDAC cases are not detected), which show promise for clinical applications.
