Joint Shape Representation and Classification for Detecting PDAC

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 {\em joint} manner, both to exploit the knowledge that PDAC often changes the {\bf shape} of the pancreas and to prevent over-fitting. Experiments are performed on $300$ normal scans and $156$ 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.