# Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation

arXiv: https://arxiv.org/abs/2205.03644 · HTML: https://mlsanity.com/paper/2205.03644/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 7 May 2022.

## Builds on (most cited first, 13 of 13)

- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [Semi-supervised Medical Image Segmentation through Dual-task Consistency](https://mlsanity.com/paper/2009.04448/) (2020)
- [Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/1903.00348/) (2019)
- [Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency](https://mlsanity.com/paper/2012.07042/) (2020)
- [Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning](https://mlsanity.com/paper/2110.05474/) (2021)
- [Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation](https://mlsanity.com/paper/2201.08657/) (2022)
- [Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model](https://mlsanity.com/paper/1808.03887/) (2018)
- [Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training](https://mlsanity.com/paper/1804.02586/) (2018)
- [Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets](https://mlsanity.com/paper/1708.00573/) (2017)
- [RSCFed: Random Sampling Consensus Federated Semi-supervised Learning](https://mlsanity.com/paper/2203.13993/) (2022)

## Built on by (most cited first, 27 of 27)

- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images](https://mlsanity.com/paper/2211.01607/) (2022)
- [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 Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks](https://mlsanity.com/paper/2302.07579/) (2023)
- [Cyclical Self-Supervision for Semi-Supervised Ejection Fraction Prediction from Echocardiogram Videos](https://mlsanity.com/paper/2210.11291/) (2022)
- [FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.04971/) (2024)
- [Tri-Plane Mamba: Efficiently Adapting Segment Anything Model for 3D Medical Images](https://mlsanity.com/paper/2409.08492/) (2024)
- [SemiDAViL: Semi-supervised Domain Adaptation with Vision-Language Guidance for Semantic Segmentation](https://mlsanity.com/paper/2504.06389/) (2025)
- [ConUNETR: A Conditional Transformer Network for 3D Micro-CT Embryonic Cartilage Segmentation](https://mlsanity.com/paper/2402.03695/) (2024)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels](https://mlsanity.com/paper/2209.15451/) (2022)
- [Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment](https://mlsanity.com/paper/2307.12630/) (2023)
- [S&D Messenger: Exchanging Semantic and Domain Knowledge for Generic Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2407.07763/) (2024)
- [Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2410.14210/) (2024)
- [ERANet: Edge Replacement Augmentation for Semi-Supervised Meniscus Segmentation with Prototype Consistency Alignment and Conditional Self-Training](https://mlsanity.com/paper/2502.07331/) (2025)
- [Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2508.13712/) (2025)
- [C3PS: Context-aware Conditional Cross Pseudo Supervision for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2306.08275/) (2023)
- [UniCoN: Universal Conditional Networks for Multi-Age Embryonic Cartilage Segmentation with Sparsely Annotated Data](https://mlsanity.com/paper/2410.13043/) (2024)
- [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)
- [Contrastive Learning for Semi-Supervised Deep Regression with Generalized Ordinal Rankings from Spectral Seriation](https://mlsanity.com/paper/2512.09267/) (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)
- [Semantic Class Distribution Learning for Debiasing Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2603.05202/) (2026)
- [BCMDA: Bidirectional Correlation Maps Domain Adaptation for Mixed Domain Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2603.24691/) (2026)
- [SegMix:Shuffle-based Feedback Learning for Semantic Segmentation of Pathology Images](https://mlsanity.com/paper/2604.15777/) (2026)

## Abstract (arXiv)

Segmentation of 3D knee MR images is important for the assessment of osteoarthritis. Like other medical data, the volume-wise labeling of knee MR images is expertise-demanded and time-consuming; hence semi-supervised learning (SSL), particularly barely-supervised learning, is highly desirable for training with insufficient labeled data. We observed that the class imbalance problem is severe in the knee MR images as the cartilages only occupy 6% of foreground volumes, and the situation becomes worse without sufficient labeled data. To address the above problem, we present a novel framework for barely-supervised knee segmentation with noisy and imbalanced labels. Our framework leverages label distribution to encourage the network to put more effort into learning cartilage parts. Specifically, we utilize 1.) label quantity distribution for modifying the objective loss function to a class-aware weighted form and 2.) label position distribution for constructing a cropping probability mask to crop more sub-volumes in cartilage areas from both labeled and unlabeled inputs. In addition, we design dual uncertainty-aware sampling supervision to enhance the supervision of low-confident categories for efficient unsupervised learning. Experiments show that our proposed framework brings significant improvements by incorporating the unlabeled data and alleviating the problem of class imbalance. More importantly, our method outperforms the state-of-the-art SSL methods, demonstrating the potential of our framework for the more challenging SSL setting.