brain-segmentation-active-learning

name	brain-segmentation-active-learning
description	Entropy-based active learning methodology for fair and efficient brain image segmentation. Use when: (1) performing brain MRI/CT segmentation with limited labeled data, (2) needing fair segmentation across demographic groups, (3) selecting most informative samples for annotation, (4) optimizing annotation budget for medical imaging, (5) addressing bias in brain segmentation models.
arxiv_id	2605.01706
published	2026-05-03
authors	["Ghazal Danaee","Mélanie Gaillochet","Christian Desrosiers","Herve Lombaert","Sylvain Bouix"]
tags	["brain-segmentation","active-learning","entropy","fairness","medical-imaging","MRI"]

Brain Segmentation via Entropy-Based Active Learning

Methodology Overview

Active learning strategy that uses model prediction entropy to select the most informative samples for annotation, combined with fairness constraints to ensure equitable segmentation performance across demographic groups.

Core Algorithm

1. Entropy-based sample selection: Compute predictive entropy over unlabeled pool
   • H(y|x) = -\sum_k p(y=k|x) log p(y=k|x)
   • Select samples with highest entropy (most uncertain)
2. Fairness-aware selection: Ensure selected samples represent all demographic groups
   • Track per-group annotation budget
   • Penalize over-representation of any group
3. Iterative training: Retrain model after each annotation batch
   • Monitor both overall accuracy and per-group fairness metrics

Key Components

Entropy Computation

• Use model prediction uncertainty as informativeness proxy
• Monte Carlo dropout or deep ensembles for uncertainty estimation
• Normalize entropy across classes to handle class imbalance

Fairness Metric

• Demographic parity in segmentation quality (Dice score per group)
• Equal opportunity: similar false negative rates across groups
• Track worst-group performance as primary fairness indicator

Selection Strategy

• Greedy: pick highest-entropy samples until budget exhausted
• Batch: diversify within high-entropy subset using clustering
• Fair: constrain selection to maintain group representation

Usage Patterns

Pattern 1: Limited Annotation Budget

When annotation budget is constrained (e.g., 10% of data):

1. Train initial model on seed set (5-10%)
2. Compute entropy on remaining unlabeled pool
3. Select top-K highest entropy samples
4. Annotate and retrain
5. Repeat until budget exhausted

Pattern 2: Fairness-Constrained Segmentation

When ensuring fair performance across groups:

1. Define demographic groups (age, sex, scanner site, etc.)
2. Compute per-group entropy distributions
3. Select samples proportionally from each group's high-entropy tail
4. Monitor per-group Dice scores during active learning
5. Adjust selection weights if any group falls behind

Pitfalls

• Entropy can be miscalibrated: use temperature scaling or MC dropout
• Small groups may be undersampled: enforce minimum per-group quota
• Class imbalance affects entropy: normalize by class prior
• Medical domain shift: validate on held-out sites/scanners

Activation

• brain segmentation active learning
• entropy-based sample selection
• fair brain segmentation
• medical imaging active learning
• 脑分割主动学习
• 公平性脑影像分割