Multi-modal classification of polyp malignancy using CNN features with balanced class augmentation

R. Fonollá, F. van der Sommen, R.M. Schreuder, E.J. Schoon, P.H.N. de With

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

4 Citations (Scopus)

Abstract

Colorectal polyps are an indicator of colorectal cancer (CRC). Classification of polyps during colonoscopy is still a challenge for which many medical experts have come up with visual models albeit with limited success. In this paper, a classification approach is proposed to differentiate between polyp malignancy, using features extracted from the Global Average Pooling (GAP) layer of a Convolutional Neural Network (CNNs). Two recent endoscopic modalities are used to improve the algorithm prediction: Blue Laser Imaging (BLI) and Linked Color Imaging (LCI). Furthermore, a new strategy of per-class data augmentation is adopted to tackle an unbalanced class distribution and to improve the decision of the classifiers. As a result, we increase the performance compared to state-of-the-art methods (0.97 vs 0.90 AUC). Our method for automatic polyp malignancy classification facilitates future advances towards patient safety and may avoid time-consuming and costly histopathological assessment.
Original languageEnglish
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages74-78
Number of pages5
ISBN (Electronic)978-1-5386-3641-1
DOIs
Publication statusPublished - 1 Apr 2019
Event16th IEEE International Symposium on Biomedical Imaging (ISBI 2019) - Venice, Italy
Duration: 8 Apr 201911 Apr 2019
Conference number: 16

Conference

Conference16th IEEE International Symposium on Biomedical Imaging (ISBI 2019)
Abbreviated titleISBI 2019
CountryItaly
CityVenice
Period8/04/1911/04/19

Keywords

  • colorectal cancer
  • deep learning
  • svm
  • colonoscopy
  • BLI
  • CNN
  • Blue laser imaging
  • Linked color imaging
  • SVM
  • LCI
  • Polyp classification
  • Data augmentation
  • Bli

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