Quality Quantification in Deep Convolutional Neural Networks for Skin Lesion Segmentation using Fuzzy Uncertainty Measurement

Qiao Lin, Xin Chen, Chao Chen, Jonathan m. Garibaldi

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)

Abstract

Deep convolutional neural networks (DCNN)-based methods have achieved promising performance in semantic image segmentation. However, in practical applications, it is important not only to produce the segmentation result but also to inform the segmentation quality (e.g. confidence of the segmentation result). In this paper, we propose to utilize fuzzy sets for estimating segmentation uncertainty, therefore to infer the quality of segmentation produced by a DCNN model. The proposed method combines test-time augmentation and fuzzy sets to estimate an image-level uncertainty. Six different fuzziness measures are implemented and compared, in order to select the best fuzzy uncertainty metric for the proposed method. A public skin lesion dataset is used to evaluate the method. The results show a strong correlation (Pearson correlation coefficient of 0.736) between our proposed uncertainty measure and image segmentation quality measured by Dice coefficient.
Original languageEnglish
Pages1-8
DOIs
Publication statusPublished - 18 Jul 2022
Event2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) - Padua, Italy
Duration: 18 Jul 202223 Jul 2022

Conference

Conference2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Period18/07/2223/07/22

Keywords

  • fuzzy sets
  • image segmentation
  • quality quantification
  • uncertainty
  • skin lesion

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