mlsanityData to 7 Oct 2026

Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images

arXiv 2501.04217 ↗First posted 8 Jan 2025cs.CV cs.AIcited by 5

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Abstract (arXiv)

We propose a novel continual self-supervised learning method (CSSL) considering medical domain knowledge in chest CT images. Our approach addresses the challenge of sequential learning by effectively capturing the relationship between previously learned knowledge and new information at different stages. By incorporating an enhanced DER into CSSL and maintaining both diversity and representativeness within the rehearsal buffer of DER, the risk of data interference during pretraining is reduced, enabling the model to learn more richer and robust feature representations. In addition, we incorporate a mixup strategy and feature distillation to further enhance the model's ability to learn meaningful representations. We validate our method using chest CT images obtained under two different imaging conditions, demonstrating superior performance compared to state-of-the-art methods.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 18 of its 26 references that are in the map. First posted on arXiv 8 Jan 2025.

Builds on

18 of 26 references.

References · 18

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  3. 2020Dark Experience for General Continual Learning: a Strong, Simple Baselinecited by 895
  4. 2019Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveycited by 656
  5. 2018Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interferencecited by 511
  6. 20203D Self-Supervised Methods for Medical Imagingcited by 134
  7. 2021Self-supervised learning methods and applications in medical imaging analysis: A surveycited by 76
  8. 2019Multimodal Self-Supervised Learning for Medical Image Analysiscited by 57
  9. 2021UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barriercited by 55
  10. 2023Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learningcited by 34
  11. 2023Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Analysiscited by 25
  12. 2021Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregationcited by 18
6 more
  1. 2022Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structurescited by 18
  2. 2022Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Imagescited by 13
  3. 2021Self-Supervised Learning for Gastritis Detection with Gastric X-ray Imagescited by 9
  4. 2022COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Imagescited by 7
  5. 2022Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensemblingcited by 3
  6. 2022Dual Windows Are Significant: Learning from Mediastinal Window and Focusing on Lung Windowcited by 2

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Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025Continual Retinal Vision-Language Pre-training upon Incremental Imaging Modalitiescited by 5
  2. 2025Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustnesscited by 1
  3. 2025Forging a Dynamic Memory: Retrieval-Guided Continual Learning for Generalist Medical Foundation Modelscited by 1
  4. 2025Dual-Model Weight Selection and Self-Knowledge Distillation for Medical Image Classificationcited by 0
  5. 2025InvCoSS: Inversion-driven Continual Self-supervised Learning in Medical Multi-modal Image Pre-trainingcited by 0