A weighted entropy active learning method for fair brain segmentation reduces group performance disparities by 75-86% versus standard entropy on synthetic biased MRI data.
Frontiers in Computational Neuroscience , volume =
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Exploring Entropy-based Active Learning for Fair Brain Segmentation
A weighted entropy active learning method for fair brain segmentation reduces group performance disparities by 75-86% versus standard entropy on synthetic biased MRI data.