Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation

Autor:	Tan, Hai Siong, Wang, Kuancheng, Mcbeth, Rafe
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Physics - Medical Physics
Druh dokumentu:	Working Paper
Popis:	In this work, we examine the effectiveness of an uncertainty quantification framework known as Evidential Deep Learning applied in the context of biomedical image segmentation. This class of models involves assigning Dirichlet distributions as priors for segmentation labels, and enables a few distinct definitions of model uncertainties. Using the cardiac and prostate MRI images available in the Medical Segmentation Decathlon for validation, we found that Evidential Deep Learning models with U-Net backbones generally yielded superior correlations between prediction errors and uncertainties relative to the conventional baseline equipped with Shannon entropy measure, Monte-Carlo Dropout and Deep Ensemble methods. We also examined these models' effectiveness in active learning, finding that relative to the standard Shannon entropy-based sampling, they yielded higher point-biserial uncertainty-error correlations while attaining similar performances in Dice-Sorensen coefficients. These superior features of EDL models render them well-suited for segmentation tasks that warrant a critical sensitivity in detecting large model errors. Comment: 15 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.18461 Zobrazit plný text záznamu View this record from Arxiv