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research-article

A dynamic uncertainty-aware ensemble model: : Application to lung cancer segmentation in digital pathology

Authors:

Alessandro Mogetta,

U. Raghavendra,

U. Rajendra Acharya,

Filippo MolinariAuthors Info & Claims

Volume 165, Issue C

https://doi.org/10.1016/j.asoc.2024.112081

Published: 01 November 2024 Publication History

Abstract

Ensemble models have emerged as a powerful technique for improving robustness in medical image segmentation. However, traditional ensembles suffer from limitations such as under-confidence and over-reliance on poor performing models. In this work, we introduce an Adaptive Uncertainty-based Ensemble (AUE) model for tumor segmentation in histopathological slides. Our approach leverages uncertainty estimates from Monte Carlo dropout during testing to dynamically select the optimal pair of models for each whole slide image. The AUE model combines predictions from the two most reliable models (K-Net, ResNeSt, Segformer, Twins), identified through uncertainty quantification, to enhance segmentation performance. We validate the AUE model on the ACDC@LungHP challenge dataset, systematically comparing it against state-of-the-art approaches. Results demonstrate that our uncertainty-guided ensemble achieves a mean Dice score of 0.8653 and outperforms traditional ensemble techniques and top-ranked methods from the challenge by over 3 %. Our adaptive ensemble approach provides accurate and reliable lung tumor delineation in histopathology images by managing model uncertainty.

Highlights

•

Adaptive uncertainty-based ensemble model (AUE) proposed for tumor segmentation.

•

AUE outperformed traditional ensemble models by a significant margin.

•

Utilizing uncertainty estimates enhances segmentation performance.

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Index Terms

A dynamic uncertainty-aware ensemble model: Application to lung cancer segmentation in digital pathology
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
      1. Imaging
    2. Health care information systems
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Interest point and salient region detections
        Video segmentation

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

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Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 165, Issue C

Nov 2024

1386 pages

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 November 2024

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