Active test-time adaptation for continual medical image classification

Key Points

• Active test-time adaptation improves classification accuracy in medical images, especially in changing environments.
• The algorithm shows a significant accuracy increase of up to 15% compared to standard methods.
• Observational analysis evaluates model performance across diverse datasets and medical conditions.
• This adaptive approach may enhance clinical decision-making but requires further validation in real-world settings.