Joint shape representation and classification for detecting PDAC

Fengze Liu, Lingxi Xie, Yingda Xia, Elliot Fishman, Alan Yuille

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Apr 27 2018

ASJC Scopus subject areas

  • General

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