CFAB: An Online Data Augmentation to Alleviate the Spuriousness of Classification on Medical Ultrasound Images

Jianhua Huang, Kuan Huang, Meng Xu, Feifei Liu

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

Abstract

Convolutional neural networks (CNNs) may learn spurious correlations between bias features (e.g., background) and labels in image classification. The spuriousness in CNNs usually occurs in building connections between the background of images and labels. Such spurious correlation limits the generalizability of CNNs in classification tasks. Changing backgrounds and foregrounds of original samples can reduce the spuriousness in natural image classification. However, generating annotations for foreground on medical image datasets is time-consuming and labor-intensive. To solve this problem, we propose an online data augmentation method named Combining Foreground And Background (CFAB), which makes CNNs focus on key causal features without foreground annotations and breaks the correlation between backgrounds and labels by changing different backgrounds for one sample. Furthermore, we propose a framework for collaborative augmenting samples using CFAB and training CNNs. Comprehensive experiments indicate that the proposed method weakens the spuriousness, improves the generalizability of the model, and achieves state-of-the-art results in medical ultrasound dataset classification.

Original languageEnglish
Title of host publicationComputer Vision Systems - 14th International Conference, ICVS 2023, Proceedings
EditorsHenrik I. Christensen, Peter Corke, Renaud Detry, Jean-Baptiste Weibel, Markus Vincze
PublisherSpringer Science and Business Media Deutschland GmbH
Pages91-101
Number of pages11
ISBN (Print)9783031441363
DOIs
StatePublished - 2023
Event14th International Conference on Computer Vision Systems, ICVS 2023 - VIenna, Austria
Duration: 27 Sep 202329 Sep 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14253 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Computer Vision Systems, ICVS 2023
Country/TerritoryAustria
CityVIenna
Period27/09/2329/09/23

Keywords

  • Classification
  • Data Augmentation
  • Medical Ultrasound Image
  • Spurious Correlation

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