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Stronger is not better: Better Augmentations in Contrastive Learning for Medical Image Segmentation

arXiv 2512.05992 ↗First posted 30 Nov 2025eess.IV cs.CVcited by 0

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Abstract (arXiv)

Self-supervised contrastive learning is among the recent representation learning methods that have shown performance gains in several downstream tasks including semantic segmentation. This paper evaluates strong data augmentation, one of the most important components for self-supervised contrastive learning's improved performance. Strong data augmentation involves applying the composition of multiple augmentation techniques on images. Surprisingly, we find that the existing data augmentations do not always improve performance for semantic segmentation for medical images. We experiment with other augmentations that provide improved performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 13 references that are in the map. First posted on arXiv 30 Nov 2025.

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References · 11

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  3. 2020Contrastive Learning of Medical Visual Representations from Paired Images and Textcited by 636
  4. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 588
  5. 2020Dense Contrastive Learning for Self-Supervised Visual Pre-Trainingcited by 512
  6. 2020Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learningcited by 297
  7. 2021Unsupervised Semantic Segmentation by Contrasting Object Mask Proposalscited by 190
  8. 2018Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysiscited by 178
  9. 2020Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representationscited by 69
  10. 2020FocalMix: Semi-Supervised Learning for 3D Medical Image Detectioncited by 52
  11. 2021SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypescited by 4

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