TY - GEN
T1 - Joint Shape Representation and Classification for Detecting PDAC
AU - Liu, Fengze
AU - Xie, Lingxi
AU - Xia, Yingda
AU - Fishman, Elliot
AU - Yuille, Alan
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85075673260&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075673260&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32692-0_25
DO - 10.1007/978-3-030-32692-0_25
M3 - Conference contribution
AN - SCOPUS:85075673260
SN - 9783030326913
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 212
EP - 220
BT - Machine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings
A2 - Suk, Heung-Il
A2 - Liu, Mingxia
A2 - Lian, Chunfeng
A2 - Yan, Pingkun
PB - Springer
T2 - 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 13 October 2019
ER -