An Empirical Study on Variance-based MC Dropout Uncertainty-Error Correlation in 2D Brain Tumor Segmentation
arXiv:2510.15541v2 Announce Type: replace Abstract: Accurate brain tumor segmentation from MRI is vital for diagnosis and treatment planning. Although Monte Carlo (MC) Dropout is widely used to estimate model uncertainty, the effectiveness of variance-based uncertainty - computed as pixel-wise variance across stochastic forward passes - in identifying segmentation errors, particularly near tumor boundaries, remains insufficiently studied. This study empirically examines the relationship between variance-based MC Dropout uncertainty and segmentation error in 2D brain tumor MRI segmentation usin
The increasing adoption of AI in critical fields like medicine necessitates robust uncertainty quantification methods for model reliability and safety.
Improving the accuracy and reliability of AI models, particularly in medical imaging, accelerates their clinical integration and reduces diagnostic errors.
This research refines the understanding and application of uncertainty estimation techniques in medical AI, potentially leading to more trustworthy diagnostic tools.
- · Medical AI developers
- · Healthcare providers
- · Patients with brain tumors
- · AI safety researchers
- · Traditional diagnostic methods
- · AI models lacking uncertainty estimation
Improved reliability and interpretability of AI-driven medical diagnostic tools.
Faster and more accurate treatment planning for conditions like brain tumors.
Increased patient confidence in AI applications within healthcare, accelerating broader adoption.
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Read at arXiv cs.LG