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Exploring Entropy-based Active Learning for Fair Brain Segmentation
Ghazal Danaee
, Mélanie Gaillochet
,
Christian Desrosiers
,
Hervé Lombaert
,
Sylvain Bouix
École de technologie supérieure
Software and Information Technology Engineering Department
itechsanté - Research Institute for Innovation in Health Technologies
LIVIA - Imaging, Vision and Artificial Intelligence Laboratory
École de technologie supérieure
Polytechnique Montréal
Université de Montréal
Research output
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Contribution to journal
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Conference article
›
peer-review
Overview
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Keyphrases
3D U-Net
20%
Active Learning
100%
Active Learning Framework
20%
Active Learning Methods
20%
Active Learning Strategies
20%
Bias Setting
20%
Brain MRI
20%
Brain Segmentation
100%
Classification Task
20%
Current Group
20%
Efficient Strategy
20%
Entropy-based Approach
100%
Epistemic Uncertainty
20%
Fairness-aware
20%
Group-specific
20%
Label Set
40%
Learning Cycle
20%
Left Caudate
40%
Medical Image Segmentation
60%
On-state Current
20%
Performance Disparities
40%
Performance Estimate
20%
Performance Fairness
20%
Performance Metrics
20%
Region of Interest
20%
Resource-constrained Environments
20%
Scaling Entropy
20%
Selection Strategy
20%
Sensitive Attribute
20%
Specific Performance
20%
Standard Entropy
20%
T1-weighted
20%
Uncertainty Sampling
20%
Weighted Entropy
20%
Computer Science
Active Learning
100%
Classification Task
12%
Constrained Environment
12%
Entropy
100%
Image Segmentation
37%
Learning Framework
12%
Performance Metric
12%
Selection Strategy
12%
Sensitive Attribute
12%
U-Net
12%
Social Sciences
Entropy
100%
Image Segmentation
75%
Learning Method
25%
Learning Strategy
25%
Magnetic Resonance Imaging
25%
U-Net
25%
Variance
25%