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Uncertainty Quantification in Segmenting Tuberculosis-Consistent Findings in Frontal Chest X-rays.

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Optimizing deep learning for tuberculosis detection in chest X-rays involves selecting the right loss function and using Monte Carlo Dropout for uncertainty quantification. An uncertainty threshold of 0.7 helps identify cases needing expert review.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Deep learning (DL) excels in medical image segmentation but requires careful loss function selection for optimal performance.
  • Traditional DL models lack uncertainty quantification, hindering trust in automated medical diagnostics.
  • Quantifying prediction uncertainty is crucial for reliable medical applications.

Purpose of the Study:

  • To investigate the benefits of an appropriate loss function and uncertainty quantification for segmenting tuberculosis (TB)-consistent findings in chest X-rays (CXRs).
  • To evaluate a VGG16-based-U-Net model incorporating Monte Carlo (MC) Dropout for uncertainty estimation.
  • To establish an optimal uncertainty threshold for referring uncertain cases to experts.

Main Methods:

  • Utilized a VGG16-based-U-Net architecture for CXR segmentation.
  • Employed a modified Focal Tversky loss function for improved segmentation performance.
  • Implemented Monte Carlo Dropout with 30 forward passes for uncertainty quantification.
  • Determined an optimal uncertainty threshold using various uncertainty metrics.

Main Results:

  • The modified Focal Tversky loss function enhanced segmentation performance (mAP: 0.5710).
  • Monte Carlo Dropout further improved and stabilized performance (mAP: 0.5721).
  • An uncertainty threshold of 0.7 was identified as optimal for case referral.

Conclusions:

  • The combination of an optimized loss function and uncertainty quantification significantly improves TB detection in CXRs.
  • The proposed method provides a reliable mechanism for identifying and escalating uncertain cases to human experts.
  • This approach enhances the trustworthiness and clinical utility of AI-driven diagnostic tools in radiology.