Labeling cost sensitive batch active learning for brain tumor segmentation

Maohao Shen, Jacky Y. Zhang, Leihao Chen, Weiman Yan, Neel Jani, Brad Sutton, Oluwasanmi Oluseye Koyejo

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

Abstract

Over the last decade, deep learning methods have achieved state-of-the-art for medical image segmentation tasks. However, the difficulty of obtaining sufficient labeled data can be a bottleneck. To this end, we design a novel active learning framework specially adapted to the brain tumor segmentation. Our approach includes a novel labeling cost designed to capture radiologists' practical labeling costs. This is combined with two acquisition functions to incorporate uncertainty and representation information, ensuring that the active learning selects informative and diverse data. The resulting procedure is a constrained combinatorial optimization problem. We propose an efficient algorithm for this task and demonstrate the proposed method's advantages for segmenting brain MRI data.

Original languageEnglish (US)
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PublisherIEEE Computer Society
Pages1269-1273
Number of pages5
ISBN (Electronic)9781665412469
DOIs
StatePublished - Apr 13 2021
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: Apr 13 2021Apr 16 2021

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2021-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Country/TerritoryFrance
CityNice
Period4/13/214/16/21

Keywords

  • Active Learning
  • Approximation Algorithm
  • Deep Learning
  • Segmentation
  • Uncertainty

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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