Selective Learning from External Data for CT Image Segmentation

Youyi Song, Lequan Yu, Baiying Lei, Kup Sze Choi, Jing Qin

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


Learning from external data is an effective and efficient way of training deep networks, which can substantially alleviate the burden on collecting training data and annotations. It is of great significance in improving the performance of CT image segmentation tasks, where collecting a large amount of voxel-wise annotations is expensive or even impractical. In this paper, we propose a generic selective learning method to maximize the performance gains of harnessing external data in CT image segmentation. The key idea is to learn a weight for each external data such that ‘good’ data can have large weights and thus contribute more to the training loss, thereby implicitly encouraging the network to mine more valuable knowledge from informative external data while suppressing to memorize irrelevant patterns from ‘useless’ or even ‘harmful’ data. Particularly, we formulate our idea as a constrained non-linear programming problem, solved by an iterative solution that alternatively conducts weights estimating and network updating. Extensive experiments on abdominal multi-organ CT segmentation datasets show the efficacy and performance gains of our method against existing methods. The code is publicly available (Released at ).

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
EditorsMarleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030871925
Publication statusPublished - Sept 2021
Event24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 27 Sept 20211 Oct 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12901 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online


  • Constrained non-linear programming
  • CT image segmentation
  • External data
  • Selective learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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