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Bayesian uncertainty-weighted loss for improved generalisability on polyp segmentation task

arXiv 2309.06807 ↗First posted 13 Sep 2023cs.CV cs.AIcited by 0

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Abstract (arXiv)

While several previous studies have devised methods for segmentation of polyps, most of these methods are not rigorously assessed on multi-center datasets. Variability due to appearance of polyps from one center to another, difference in endoscopic instrument grades, and acquisition quality result in methods with good performance on in-distribution test data, and poor performance on out-of-distribution or underrepresented samples. Unfair models have serious implications and pose a critical challenge to clinical applications. We adapt an implicit bias mitigation method which leverages Bayesian predictive uncertainties during training to encourage the model to focus on underrepresented sample regions. We demonstrate the potential of this approach to improve generalisability without sacrificing state-of-the-art performance on a challenging multi-center polyp segmentation dataset (PolypGen) with different centers and image modalities.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 35 references that are in the map. First posted on arXiv 13 Sep 2023.

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27 of 35 references.

References · 27

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15 more
  1. 2019Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learningcited by 145
  2. 2019Striking the Right Balance with Uncertaintycited by 79
  3. 2021A multi-centre polyp detection and segmentation dataset for generalisability assessmentcited by 63
  4. 2021Fairness via Representation Neutralizationcited by 63
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  15. 2022Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigationcited by 11

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