# Continual Learning for Domain Adaptation in Chest X-ray Classification

arXiv: https://arxiv.org/abs/2001.05922 · HTML: https://mlsanity.com/paper/2001.05922/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2020.

## Built on by (most cited first, 30 of 31)

- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [Online Coreset Selection for Rehearsal-based Continual Learning](https://mlsanity.com/paper/2106.01085/) (2021)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [LifeLonger: A Benchmark for Continual Disease Classification](https://mlsanity.com/paper/2204.05737/) (2022)
- [ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis](https://mlsanity.com/paper/2308.13324/) (2023)
- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects](https://mlsanity.com/paper/2312.17004/) (2023)
- [Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation](https://mlsanity.com/paper/2107.08751/) (2021)
- [BiasPruner: Debiased Continual Learning for Medical Image Classification](https://mlsanity.com/paper/2407.08609/) (2024)
- [A Continual Learning Approach for Cross-Domain White Blood Cell Classification](https://mlsanity.com/paper/2308.12679/) (2023)
- [Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition](https://mlsanity.com/paper/2106.03351/) (2021)
- [Continual Learning in Medical Imaging: A Survey and Practical Analysis](https://mlsanity.com/paper/2405.13482/) (2024)
- [Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data](https://mlsanity.com/paper/2205.03168/) (2022)
- [Domain-Adaptive 3D Medical Image Synthesis: An Efficient Unsupervised Approach](https://mlsanity.com/paper/2207.00844/) (2022)
- [Generative appearance replay for continual unsupervised domain adaptation](https://mlsanity.com/paper/2301.01211/) (2023)
- [Continual Learning for Tumor Classification in Histopathology Images](https://mlsanity.com/paper/2208.03609/) (2022)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark](https://mlsanity.com/paper/2404.06859/) (2024)
- [Re-examining Distillation For Continual Object Detection](https://mlsanity.com/paper/2204.01407/) (2022)
- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing](https://mlsanity.com/paper/2210.15051/) (2022)
- [Few-shot Class-incremental Learning for Cross-domain Disease Classification](https://mlsanity.com/paper/2304.05734/) (2023)
- [Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics](https://mlsanity.com/paper/2111.13069/) (2021)
- [MLHOps: Machine Learning for Healthcare Operations](https://mlsanity.com/paper/2305.02474/) (2023)
- [Fairness Evolution in Continual Learning for Medical Imaging](https://mlsanity.com/paper/2406.02480/) (2024)
- [Is user feedback always informative? Retrieval Latent Defending for Semi-Supervised Domain Adaptation without Source Data](https://mlsanity.com/paper/2407.15383/) (2024)
- [Trustworthy Representation Learning Across Domains](https://mlsanity.com/paper/2308.12315/) (2023)
- [Continuous Unsupervised Domain Adaptation Using Stabilized Representations and Experience Replay](https://mlsanity.com/paper/2402.00580/) (2024)
- [External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data](https://mlsanity.com/paper/2201.00942/) (2022)
- [From Lab to Pocket: A Novel Continual Learning-based Mobile Application for Screening COVID-19](https://mlsanity.com/paper/2410.12589/) (2024)

## Abstract (arXiv)

Over the last years, Deep Learning has been successfully applied to a broad range of medical applications. Especially in the context of chest X-ray classification, results have been reported which are on par, or even superior to experienced radiologists. Despite this success in controlled experimental environments, it has been noted that the ability of Deep Learning models to generalize to data from a new domain (with potentially different tasks) is often limited. In order to address this challenge, we investigate techniques from the field of Continual Learning (CL) including Joint Training (JT), Elastic Weight Consolidation (EWC) and Learning Without Forgetting (LWF). Using the ChestX-ray14 and the MIMIC-CXR datasets, we demonstrate empirically that these methods provide promising options to improve the performance of Deep Learning models on a target domain and to mitigate effectively catastrophic forgetting for the source domain. To this end, the best overall performance was obtained using JT, while for LWF competitive results could be achieved - even without accessing data from the source domain.