# Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition

arXiv: https://arxiv.org/abs/2106.03351 · HTML: https://mlsanity.com/paper/2106.03351/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1910.02923/) (2019)
- [ACE: Adapting to Changing Environments for Semantic Segmentation](https://mlsanity.com/paper/1904.06268/) (2019)
- [Continual Learning for Domain Adaptation in Chest X-ray Classification](https://mlsanity.com/paper/2001.05922/) (2020)
- [What is Wrong with Continual Learning in Medical Image Segmentation?](https://mlsanity.com/paper/2010.11008/) (2020)
- [Learn the new, keep the old: Extending pretrained models with new anatomy and images](https://mlsanity.com/paper/1806.00265/) (2018)
- [Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay](https://mlsanity.com/paper/2003.04382/) (2020)
- [Dynamic memory to alleviate catastrophic forgetting in continuous learning settings](https://mlsanity.com/paper/2007.02639/) (2020)

## Built on by (most cited first, 9 of 9)

- [Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects](https://mlsanity.com/paper/2312.17004/) (2023)
- [Domain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whitening](https://mlsanity.com/paper/2211.04862/) (2022)
- [Beyond Supervised Continual Learning: a Review](https://mlsanity.com/paper/2208.14307/) (2022)
- [Accelerating Batch Active Learning Using Continual Learning Techniques](https://mlsanity.com/paper/2305.06408/) (2023)
- [Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts](https://mlsanity.com/paper/2208.03206/) (2022)
- [Active Learning for Continual Learning: Keeping the Past Alive in the Present](https://mlsanity.com/paper/2501.14278/) (2025)
- [Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics](https://mlsanity.com/paper/2111.13069/) (2021)
- [Towards Synchronous Memorizability and Generalizability with Site-Modulated Diffusion Replay for Cross-Site Continual Segmentation](https://mlsanity.com/paper/2406.18037/) (2024)
- [Active Continual Learning with Metaplastic Binary Bayesian Neural Networks](https://mlsanity.com/paper/2605.30198/) (2026)

## Abstract (arXiv)

Imaging in clinical routine is subject to changing scanner protocols, hardware, or policies in a typically heterogeneous set of acquisition hardware. Accuracy and reliability of deep learning models suffer from those changes as data and targets become inconsistent with their initial static training set. Continual learning can adapt to a continuous data stream of a changing imaging environment. Here, we propose a method for continual active learning on a data stream of medical images. It recognizes shifts or additions of new imaging sources - domains -, adapts training accordingly, and selects optimal examples for labelling. Model training has to cope with a limited labelling budget, resembling typical real world scenarios. We demonstrate our method on T1-weighted magnetic resonance images from three different scanners with the task of brain age estimation. Results demonstrate that the proposed method outperforms naive active learning while requiring less manual labelling.