mlsanityData to 14 Oct 2026

MEGAN: Mixture of Experts for Robust Uncertainty Estimation in Endoscopy Videos

arXiv 2509.12772 ↗First posted 16 Sep 2025eess.IV cs.AI cs.CVcited by 0

On the map: Mixture-of-experts → Medical and biomedical MoE

Abstract (arXiv)

Reliable uncertainty quantification (UQ) is essential in medical AI. Evidential Deep Learning (EDL) offers a computationally efficient way to quantify model uncertainty alongside predictions, unlike traditional methods such as Monte Carlo (MC) Dropout and Deep Ensembles (DE). However, all these methods often rely on a single expert's annotations as ground truth for model training, overlooking the inter-rater variability in healthcare. To address this issue, we propose MEGAN, a Multi-Expert Gating Network that aggregates uncertainty estimates and predictions from multiple AI experts via EDL models trained with diverse ground truths and modeling strategies. MEGAN's gating network optimally combines predictions and uncertainties from each EDL model, enhancing overall prediction confidence and calibration. We extensively benchmark MEGAN on endoscopy videos for Ulcerative colitis (UC) disease severity estimation, assessed by visual labeling of Mayo Endoscopic Subscore (MES), where inter-rater variability is prevalent. In large-scale prospective UC clinical trial, MEGAN achieved a 3.5% improvement in F1-score and a 30.5% reduction in Expected Calibration Error (ECE) compared to existing methods. Furthermore, MEGAN facilitated uncertainty-guided sample stratification, reducing the annotation burden and potentially increasing efficiency and consistency in UC trials.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts. Builds on 17 of its 26 references that are in the map. First posted on arXiv 16 Sep 2025.

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17 of 26 references.

References · 17

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  4. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  5. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  6. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 732
  7. 2023Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-traincited by 75
  8. 2022TBraTS: Trusted Brain Tumor Segmentationcited by 30
  9. 2020Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessmentcited by 26
  10. 2019Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessmentcited by 23
  11. 2023Self-Supervised Learning for Endoscopic Video Analysiscited by 22
  12. 2022Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detectioncited by 20
5 more
  1. 2022Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimationcited by 17
  2. 2025EndoDINO: A Foundation Model for GI Endoscopycited by 9
  3. 2021Evidential segmentation of 3D PET/CT imagescited by 8
  4. 2021Automatic Estimation of Ulcerative Colitis Severity from Endoscopy Videos using Ordinal Multi-Instance Learningcited by 7
  5. 2024Arges: Spatio-Temporal Transformer for Ulcerative Colitis Severity Assessment in Endoscopy Videoscited by 1

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