  We propose an intuitive approach of detecting pancreatic ductal
adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking
abdominal CT scans. Our idea is named multi-scale
segmentation-for-classification, which classifies volumes by checking if at
least a sufficient number of voxels is segmented as tumors, by which we can
provide radiologists with tumor locations. In order to deal with tumors with
different scales, we train and test our volumetric segmentation networks with
multi-scale inputs in a coarse-to-fine flowchart. A post-processing module is
used to filter out outliers and reduce false alarms. We collect a new dataset
containing 439 CT scans, in which 136 cases were diagnosed with PDAC and 303
cases are normal, which is the largest set for PDAC tumors to the best of our
knowledge. To offer the best trade-off between sensitivity and specificity, our
proposed framework reports a sensitivity of 94.1% at a specificity of 98.5%,
which demonstrates the potential to make a clinical impact.
