Partly Supervised Multi-Task Learning

Abdullah Al Zubaer Imran, Chao Huang, Hui Tang, Wei Fan, Yuan Xiao, Dingjun Hao, Zhen Qian, Demetri Terzopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Semi-supervised learning has recently been attracting attention as an alternative to fully supervised models that require large pools of labeled data. Moreover, optimizing a model for multiple tasks can provide better generalizability than single-task learning. Leveraging self-supervision and adversarial training, we propose a novel, general purpose semi-supervised, multiple-task model - namely, self-supervised, semi-supervised, multi-task learning (S4MTL) - for accomplishing two important medical image analysis tasks: segmentation and diagnostic classification. Experimental results on chest and spine X-ray datasets confirm that our S4MTL model significantly outperforms semi-supervised single-task, semi/fully-supervised multi-task, and fully-supervised single-task models, even with a 50% reduction in class and segmentation labels.

Original languageEnglish
Title of host publicationProceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
EditorsM. Arif Wani, Feng Luo, Xiaolin Li, Dejing Dou, Francesco Bonchi
Pages769-774
Number of pages6
ISBN (Electronic)9781728184708
DOIs
StatePublished - Dec 2020
Event19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 - Virtual, Miami, United States
Duration: Dec 14 2020Dec 17 2020

Publication series

NameProceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020

Conference

Conference19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
Country/TerritoryUnited States
CityVirtual, Miami
Period12/14/2012/17/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • X-ray
  • chest
  • classification
  • multi-task learning
  • segmentation
  • self-supervision
  • semi-supervised learning
  • spine

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture

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