mlsanityData to 7 Oct 2026

Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation

arXiv 2202.02371 ↗First posted 4 Feb 2022eess.IV cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

Unsupervised pre-training has been proven as an effective approach to boost various downstream tasks given limited labeled data. Among various methods, contrastive learning learns a discriminative representation by constructing positive and negative pairs. However, it is not trivial to build reasonable pairs for a segmentation task in an unsupervised way. In this work, we propose a novel unsupervised pre-training framework that avoids the drawback of contrastive learning. Our framework consists of two principles: unsupervised over-segmentation as a pre-train task using mutual information maximization and boundary-aware preserving learning. Experimental results on two benchmark medical segmentation datasets reveal our method's effectiveness in improving segmentation performance when few annotated images are available.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 40 of its 55 references that are in the map. First posted on arXiv 4 Feb 2022.

Builds on

40 of 55 references.

References · 40

  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. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  4. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  5. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  6. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  7. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
  8. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
  9. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,586
  10. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  11. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,379
  12. 2018Learning deep representations by mutual information estimation and maximizationcited by 1,265
28 more
  1. 2016Colorful Image Colorizationcited by 1,113
  2. 2019On the Variance of the Adaptive Learning Rate and Beyondcited by 763
  3. 2020Self-supervised Learning: Generative or Contrastivecited by 693
  4. 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 657
  5. 2019Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveycited by 656
  6. 2018ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentationcited by 623
  7. 2018Invariant Information Clustering for Unsupervised Image Classification and Segmentationcited by 454
  8. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 378
  9. 2020Contrastive learning of global and local features for medical image segmentation with limited annotationscited by 309
  10. 2019Interpolation Consistency Training for Semi-Supervised Learningcited by 248
  11. 2021Unsupervised Semantic Segmentation by Contrasting Object Mask Proposalscited by 190
  12. 2018Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysiscited by 178
  13. 2021PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clusteringcited by 163
  14. 2020Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Imagescited by 152
  15. 2017Learning Discrete Representations via Information Maximizing Self-Augmented Trainingcited by 149
  16. 20203D Self-Supervised Methods for Medical Imagingcited by 134
  17. 2020Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotationcited by 128
  18. 2019Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Predictioncited by 70
  19. 2019Multimodal Self-Supervised Learning for Medical Image Analysiscited by 57
  20. 2021Positional Contrastive Learning for Volumetric Medical Image Segmentationcited by 56
  21. 2021Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentationcited by 52
  22. 2021Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentationcited by 43
  23. 2019Shape-Aware Organ Segmentation by Predicting Signed Distance Mapscited by 41
  24. 2021Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labelscited by 32
  25. 2018Deep semi-supervised segmentation with weight-averaged consistency targetscited by 21
  26. 2021Medical Image Segmentation with Limited Supervision: A Review of Deep Network Modelscited by 21
  27. 2021Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularizationcited by 14
  28. 2021A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentationcited by 14

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023Exploring Semantic Consistency in Unpaired Image Translation to Generate Data for Surgical Applicationscited by 9
  2. 2024Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning Methodcited by 0