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Continual domain incremental learning for chest X-ray classification in low-resource clinical settings
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Shikhar Srivastava, Mohamed Bin Zayed University of Artificial Intelligence
  • Mohammad Yaqub, Mohamed Bin Zayed University of Artificial Intelligence
  • Karthik Nandakumar, Mohamed Bin Zayed University of Artificial Intelligence
  • Zongyuan Ge, Monash University & Airdoc Research
  • Dwarikanath Mahapatra, Inception Institute of Artificial Intelligence
Document Type
Conference Proceeding

Within clinical practise, a shift in the distribution of data collected over time is commonly observed. This occurs generally due to deliberate changes in acquisition hardware but also through natural, unforeseen shifts in the hardware’s physical properties like scanner SNR and gradient non-linearities. These domain shifts thus may not be known a-priori, but may cause significant degradation in the diagnostic performance of machine learning models. A deployed diagnostic system must therefore be robust to such unpredictable and continuous domain shifts. However, given the infrastructure and resource constraints pervasive in clinical settings in developing countries, such robustness must be achieved under finite memory and data privacy constraints. In this work, we propose a domain-incremental learning approach that leverages vector quantization to efficiently store and replay hidden representations under limited memory constraints. Our proposed approach validated on well known large-scale public Chest X-ray datasets achieves significant reduction in catastrophic forgetting over previous approaches in medical imaging, while requiring no prior knowledge of domain shift boundaries and a constrained memory. Finally, we also formulate a more natural continual learning setting for medical imaging using a tapered uniform distribution schedule with gradual interleaved domain shifts.

Publication Date
  • Catastrophic forgetting,
  • Chest X-ray classification,
  • Continuous learning,
  • Domain adaptation

IR Deposit conditions:

  • OA version (pathway a)
  • Accepted version 12 month embargo
  • Must link to published article
  • Set statement to accompany deposit
Citation Information
S. Srivastava, M. Yaqub, K. Nandakumar, Z. Ge and D. Mahapatra, "Continual domain incremental learning for chest X-ray classification in low-resource clinical settings", in Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health, DART 2021, FAIR 2021, (Lecture Notes in Computer Science, v.12968), pp. 226-238, 2021. Available: 10.1007/978-3-030-87722-4_21