# Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI

arXiv: https://arxiv.org/abs/2209.00123 · HTML: https://mlsanity.com/paper/2209.00123/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 31 Aug 2022.

## Builds on (most cited first, 16 of 16)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Contrastive learning of global and local features for medical image segmentation with limited annotations](https://mlsanity.com/paper/2006.10511/) (2020)
- [Semi-supervised Medical Image Segmentation through Dual-task Consistency](https://mlsanity.com/paper/2009.04448/) (2020)
- [Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax](https://mlsanity.com/paper/2006.10408/) (2020)
- [Guided Collaborative Training for Pixel-wise Semi-Supervised Learning](https://mlsanity.com/paper/2008.05258/) (2020)
- [Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning](https://mlsanity.com/paper/2110.05474/) (2021)
- [Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation](https://mlsanity.com/paper/2006.16806/) (2020)
- [Deep Co-Training for Semi-Supervised Image Segmentation](https://mlsanity.com/paper/1903.11233/) (2019)
- [Positional Contrastive Learning for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2106.09157/) (2021)
- [Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation](https://mlsanity.com/paper/2109.07407/) (2021)
- [Semi-Supervised and Task-Driven Data Augmentation](https://mlsanity.com/paper/1902.05396/) (2019)
- [Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels](https://mlsanity.com/paper/2107.13741/) (2021)
- [Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection](https://mlsanity.com/paper/2106.04919/) (2021)
- [An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2202.00677/) (2022)
- [MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation](https://mlsanity.com/paper/2203.14341/) (2022)
- [DFENet: A Novel Dimension Fusion Edge Guided Network for Brain MRI Segmentation](https://mlsanity.com/paper/2105.07962/) (2021)

## Built on by (most cited first, 14 of 14)

- [Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2310.11320/) (2023)
- [DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2307.11960/) (2023)
- [Semi-supervised Domain Adaptive Medical Image Segmentation through Consistency Regularized Disentangled Contrastive Learning](https://mlsanity.com/paper/2307.02798/) (2023)
- [IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2210.15075/) (2022)
- [SemiDAViL: Semi-supervised Domain Adaptation with Vision-Language Guidance for Semantic Segmentation](https://mlsanity.com/paper/2504.06389/) (2025)
- [Exploring Self-Supervised Representation Learning For Low-Resource Medical Image Analysis](https://mlsanity.com/paper/2303.02245/) (2023)
- [S&D Messenger: Exchanging Semantic and Domain Knowledge for Generic Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2407.07763/) (2024)
- [Affinity-Graph-Guided Contractive Learning for Pretext-Free Medical Image Segmentation with Minimal Annotation](https://mlsanity.com/paper/2410.10366/) (2024)
- [Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2410.14210/) (2024)
- [An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis](https://mlsanity.com/paper/2303.05556/) (2023)
- [Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation](https://mlsanity.com/paper/2501.13470/) (2025)
- [Boosting Semi-Supervised Medical Image Segmentation via Masked Image Consistency and Discrepancy Learning](https://mlsanity.com/paper/2503.14013/) (2025)
- [JanusNet: Hierarchical Slice-Block Shuffle and Displacement for Semi-Supervised 3D Multi-Organ Segmentation](https://mlsanity.com/paper/2508.03997/) (2025)
- [DINO-Mix: Distilling Foundational Knowledge with Cross-Domain CutMix for Semi-supervised Class-imbalanced Medical Image Segmentation](https://mlsanity.com/paper/2602.07819/) (2026)

## Abstract (arXiv)

Due to the imbalanced and limited data, semi-supervised medical image segmentation methods often fail to produce superior performance for some specific tailed classes. Inadequate training for those particular classes could introduce more noise to the generated pseudo labels, affecting overall learning. To alleviate this shortcoming and identify the under-performing classes, we propose maintaining a confidence array that records class-wise performance during training. A fuzzy fusion of these confidence scores is proposed to adaptively prioritize individual confidence metrics in every sample rather than traditional ensemble approaches, where a set of predefined fixed weights are assigned for all the test cases. Further, we introduce a robust class-wise sampling method and dynamic stabilization for a better training strategy. Our proposed method considers all the under-performing classes with dynamic weighting and tries to remove most of the noises during training. Upon evaluation on two cardiac MRI datasets, ACDC and MMWHS, our proposed method shows effectiveness and generalizability and outperforms several state-of-the-art methods found in the literature.