mlsanityData to 14 Oct 2026

One-shot Weakly-Supervised Segmentation in Medical Images

arXiv 2111.10773 ↗First posted 21 Nov 2021eess.IV cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation. One-shot segmentation and weakly-supervised learning are promising research directions that lower labeling effort by learning a new class from only one annotated image and utilizing coarse labels instead, respectively. Previous works usually fail to leverage the anatomical structure and suffer from class imbalance and low contrast problems. Hence, we present an innovative framework for 3D medical image segmentation with one-shot and weakly-supervised settings. Firstly a propagation-reconstruction network is proposed to project scribbles from annotated volume to unlabeled 3D images based on the assumption that anatomical patterns in different human bodies are similar. Then a dual-level feature denoising module is designed to refine the scribbles based on anatomical- and pixel-level features. After expanding the scribbles to pseudo masks, we could train a segmentation model for the new class with the noisy label training strategy. Experiments on one abdomen and one head-and-neck CT dataset show the proposed method obtains significant improvement over the state-of-the-art methods and performs robustly even under severe class imbalance and low contrast.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 59 of its 71 references that are in the map. First posted on arXiv 21 Nov 2021.

Builds on

59 of 71 references.

References · 59

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  5. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  6. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
  7. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  8. 2016Matching Networks for One Shot Learningcited by 2,255
  9. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,391
  10. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  11. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,059
  12. 2018Learning to Reweight Examples for Robust Deep Learningcited by 675
38 more
  1. 2019Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveycited by 657
  2. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
  3. 2017Robust Loss Functions under Label Noise for Deep Neural Networkscited by 443
  4. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 379
  5. 2017One-Shot Learning for Semantic Segmentationcited by 337
  6. 2016ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentationcited by 331
  7. 2018Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentationcited by 321
  8. 2020Contrastive learning of global and local features for medical image segmentation with limited annotationscited by 309
  9. 2017Meta Networkscited by 269
  10. 2019Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentationcited by 260
  11. 2018Probabilistic Model-Agnostic Meta-Learningcited by 256
  12. 2016One-Shot Video Object Segmentationcited by 235
  13. 2016Simple Does It: Weakly Supervised Instance and Semantic Segmentationcited by 227
  14. 2019Understanding and Utilizing Deep Neural Networks Trained with Noisy Labelscited by 209
  15. 2021Adaptive Prototype Learning and Allocation for Few-Shot Segmentationcited by 207
  16. 2019Causality matters in medical imagingcited by 193
  17. 2019Deep learning with noisy labels: exploring techniques and remedies in medical image analysiscited by 173
  18. 2018SG-One: Similarity Guidance Network for One-Shot Semantic Segmentationcited by 169
  19. 2018Task-Agnostic Meta-Learning for Few-shot Learningcited by 151
  20. 2019Data augmentation using learned transformations for one-shot medical image segmentationcited by 138
  21. 2020Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotationcited by 128
  22. 2017DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentationcited by 110
  23. 2021Learning with Feature-Dependent Label Noise: A Progressive Approachcited by 102
  24. 2018Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registrationcited by 90
  25. 2016DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networkscited by 79
  26. 2019Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Predictioncited by 70
  27. 2019'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Imagescited by 69
  28. 2019Robust Inference via Generative Classifiers for Handling Noisy Labelscited by 68
  29. 2020SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Imagescited by 67
  30. 2021Recurrent Mask Refinement for Few-Shot Medical Image Segmentationcited by 63
  31. 2018Constrained-CNN losses for weakly supervised segmentationcited by 62
  32. 2016One-shot Learning with Memory-Augmented Neural Networkscited by 60
  33. 2014Training Convolutional Networks with Noisy Labelscited by 48
  34. 2020Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervisioncited by 40
  35. 2021Puzzle-CAM: Improved localization via matching partial and full featurescited by 37
  36. 2017Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networkscited by 33
  37. 2021One-Shot Medical Landmark Detectioncited by 31
  38. 2018Few-Shot Segmentation Propagation with Guided Networkscited by 29

50 most cited of 59 All 59 (JSON)

Built on by

Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Efficient Subclass Segmentation in Medical Imagescited by 0