Joint Shape Representation and Classification for Detecting PDAC
This work addresses early diagnosis of pancreatic cancer, a critical medical challenge, but it is incremental as it builds on existing segmentation and classification methods.
The paper tackles the problem of detecting pancreatic ductal adenocarcinoma (PDAC) in abdominal CT scans with limited training data, achieving a specificity of 90.2% at a sensitivity of 80.2%.
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.