# Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics

arXiv: https://arxiv.org/abs/2111.13069 · HTML: https://mlsanity.com/paper/2111.13069/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 25 Nov 2021.

## Builds on (most cited first, 22 of 22)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge](https://mlsanity.com/paper/1612.08012/) (2016)
- [Causality matters in medical imaging](https://mlsanity.com/paper/1912.08142/) (2019)
- [A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1910.02923/) (2019)
- [A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols](https://mlsanity.com/paper/1805.10170/) (2018)
- [ACE: Adapting to Changing Environments for Semantic Segmentation](https://mlsanity.com/paper/1904.06268/) (2019)
- [Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects](https://mlsanity.com/paper/1910.04597/) (2019)
- [Continual Learning for Domain Adaptation in Chest X-ray Classification](https://mlsanity.com/paper/2001.05922/) (2020)
- [LNDb: A Lung Nodule Database on Computed Tomography](https://mlsanity.com/paper/1911.08434/) (2019)
- [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)
- [Importance Driven Continual Learning for Segmentation Across Domains](https://mlsanity.com/paper/2005.00079/) (2020)
- [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)
- [Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition](https://mlsanity.com/paper/2106.03351/) (2021)
- [O-MedAL: Online Active Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1908.10508/) (2019)

## Built on by (most cited first, 3 of 3)

- [Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects](https://mlsanity.com/paper/2312.17004/) (2023)
- [Learning to Learn for Few-shot Continual Active Learning](https://mlsanity.com/paper/2311.03732/) (2023)
- [Mitigating Catastrophic Forgetting in the Incremental Learning of Medical Images](https://mlsanity.com/paper/2504.20033/) (2025)

## Abstract (arXiv)

Machine learning in medical imaging during clinical routine is impaired by changes in scanner protocols, hardware, or policies resulting in a heterogeneous set of acquisition settings. When training a deep learning model on an initial static training set, model performance and reliability suffer from changes of acquisition characteristics as data and targets may become inconsistent. Continual learning can help to adapt models to the changing environment by training on a continuous data stream. However, continual manual expert labelling of medical imaging requires substantial effort. Thus, ways to use labelling resources efficiently on a well chosen sub-set of new examples is necessary to render this strategy feasible. Here, we propose a method for continual active learning operating on a stream of medical images in a multi-scanner setting. The approach automatically recognizes shifts in image acquisition characteristics - new domains -, selects optimal examples for labelling and adapts training accordingly. Labelling is subject to a limited budget, resembling typical real world scenarios. To demonstrate generalizability, we evaluate the effectiveness of our method on three tasks: cardiac segmentation, lung nodule detection and brain age estimation. Results show that the proposed approach outperforms other active learning methods, while effectively counteracting catastrophic forgetting.