Progressive adversarial semantic segmentation

Abdullah Al Zubaer Imran, Demetri Terzopoulos

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

3 Scopus citations


Medical image computing has advanced rapidly with the advent of deep learning techniques. Deep convolutional neural networks can perform well given full supervision. However, the success of such fully-supervised models in various image analysis tasks (e.g., anatomy or lesion segmentation from medical images) depends on the availability of massive quantities of labeled data. Given small sample sizes, such models are prohibitively data biased with large domain shifts. To tackle this problem, we propose a novel end-to-end medical image segmentation model, namely Progressive Adversarial Semantic Segmentation (PASS), which can make improved and consistent pixel-wise segmentation predictions without requiring any domain-specific data during training. Our extensive experimentation with 8 public diabetic retinopathy and chest X-ray datasets confirms the effectiveness of PASS in accurate vascular and pulmonary segmentation, both for in-domain and cross-domain evaluations.

Original languageEnglish
Title of host publicationProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
Number of pages8
ISBN (Electronic)9781728188089
StatePublished - 2020
Event25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
Duration: Jan 10 2021Jan 15 2021

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference25th International Conference on Pattern Recognition, ICPR 2020
CityVirtual, Milan

Bibliographical note

Publisher Copyright:
© 2020 IEEE


  • Adversarial learning
  • Diabetic retinopathy
  • Domain-shift
  • Pulmonary X-ray
  • Retinal vasculature
  • Segmentation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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