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arXiv · 1804.10684

Joint Shape Representation and Classification for Detecting PDAC

Fengze Liu·Lingxi Xie·Yingda Xia·Elliot K. Fishman·Alan L. Yuille
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Abstract

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.

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Fengze Liu, Lingxi Xie, Yingda Xia, Elliot K. Fishman, Alan L. Yuille. 2018-04-27. Joint Shape Representation and Classification for Detecting PDAC. https://arxiv.org/abs/1804.10684

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