Joint Shape Representation and Classification for Detecting PDAC

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMachine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsHeung-Il Suk, Mingxia Liu, Chunfeng Lian, Pingkun Yan
PublisherSpringer
Pages212-220
Number of pages9
ISBN (Print)9783030326913
DOIs
StatePublished - Jan 1 2019
Event10th 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 - Shenzhen, China
Duration: Oct 13 2019Oct 13 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11861 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th 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
CountryChina
CityShenzhen
Period10/13/1910/13/19

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Liu, F., Xie, L., Xia, Y., Fishman, E., & Yuille, A. (2019). Joint Shape Representation and Classification for Detecting PDAC. In H-I. Suk, M. Liu, C. Lian, & P. Yan (Eds.), Machine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 212-220). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11861 LNCS). Springer. https://doi.org/10.1007/978-3-030-32692-0_25