Joint Semi-supervised and Active Learning for Segmentation of Gigapixel Pathology Images with Cost-Effective Labeling

Zhengfeng Lai, Chao Wang, Luca Cerny Oliveira, Brittany N. Dugger, Sen Ching Cheung, Chen Nee Chuah

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

1 Scopus citations

Abstract

The need for manual and detailed annotations limits the applicability of supervised deep learning algorithms in medical image analyses, specifically in the field of pathology. Semi-supervised learning (SSL) provides an effective way for leveraging unlabeled data to relieve the heavy reliance on the amount of labeled samples when training a model. Although SSL has shown good performance, the performance of recent state-of-the-art SSL methods on pathology images is still under study. The problem for selecting the most optimal data to label for SSL is not fully explored. To tackle this challenge, we propose a semi-supervised active learning framework with a region-based selection criterion. This framework iteratively selects regions for an-notation query to quickly expand the diversity and volume of the labeled set. We evaluate our framework on a grey-matter/white-matter segmentation problem using gigapixel pathology images from autopsied human brain tissues. With only 0.1% regions labeled, our proposed algorithm can reach a competitive IoU score compared to fully-supervised learning and outperform the current state-of-the-art SSL by more than 10% of IoU score and DICE coefficient.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages591-600
Number of pages10
ISBN (Electronic)9781665401913
DOIs
StatePublished - 2021
Event18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 - Virtual, Online, Canada
Duration: Oct 11 2021Oct 17 2021

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2021-October
ISSN (Print)1550-5499

Conference

Conference18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
Country/TerritoryCanada
CityVirtual, Online
Period10/11/2110/17/21

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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