# Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation

arXiv: https://arxiv.org/abs/2305.16214 · HTML: https://mlsanity.com/paper/2305.16214/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 25 May 2023.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [Semi-Supervised Semantic Segmentation with Cross-Consistency Training](https://mlsanity.com/paper/2003.09005/) (2020)
- [Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2101.10979/) (2021)
- [Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/1903.00348/) (2019)
- [Semi-supervised Semantic Segmentation with Directional Context-aware Consistency](https://mlsanity.com/paper/2106.14133/) (2021)
- [Semi-supervised Left Atrium Segmentation with Mutual Consistency Training](https://mlsanity.com/paper/2103.02911/) (2021)
- [Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency](https://mlsanity.com/paper/2012.07042/) (2020)
- [Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2203.01324/) (2022)
- [Uncertainty guided semi-supervised segmentation of retinal layers in OCT images](https://mlsanity.com/paper/2103.02083/) (2021)
- [All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2109.13930/) (2021)

## Built on by (most cited first, 20 of 20)

- [Adaptive Bidirectional Displacement for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2405.00378/) (2024)
- [Pathological Semantics-Preserving Learning for H&E-to-IHC Virtual Staining](https://mlsanity.com/paper/2407.03655/) (2024)
- [Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2312.16039/) (2023)
- [Uncertainty-aware Cross-training for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2508.09014/) (2025)
- [Dual Cross-image Semantic Consistency with Self-aware Pseudo Labeling for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2507.21440/) (2025)
- [ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2401.14074/) (2024)
- [Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI](https://mlsanity.com/paper/2408.05803/) (2024)
- [Leveraging Fixed and Dynamic Pseudo-labels for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2405.07256/) (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)
- [Dual-supervised Asymmetric Co-training for Semi-supervised Medical Domain Generalization](https://mlsanity.com/paper/2509.20785/) (2025)
- [Class-balanced Open-set Semi-supervised Object Detection for Medical Images](https://mlsanity.com/paper/2408.12355/) (2024)
- [SemSim: Revisiting Weak-to-Strong Consistency from a Semantic Similarity Perspective for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2410.13486/) (2024)
- [CAD: Confidence-Aware Adaptive Displacement for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2502.00536/) (2025)
- [Dual Teacher-Student Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2505.11018/) (2025)
- [P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2505.15861/) (2025)
- [Style-Aware Blending and Prototype-Based Cross-Contrast Consistency for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2507.20729/) (2025)
- [REASON: Probability map-guided dual-branch fusion framework for gastric content assessment](https://mlsanity.com/paper/2511.01302/) (2025)
- [Optimizing Neural Network Architecture for Medical Image Segmentation Using Monte Carlo Tree Search](https://mlsanity.com/paper/2602.22361/) (2026)
- [Semantic Class Distribution Learning for Debiasing Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2603.05202/) (2026)
- [SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2607.07019/) (2026)

## Abstract (arXiv)

Consistency learning plays a crucial role in semi-supervised medical image segmentation as it enables the effective utilization of limited annotated data while leveraging the abundance of unannotated data. The effectiveness and efficiency of consistency learning are challenged by prediction diversity and training stability, which are often overlooked by existing studies. Meanwhile, the limited quantity of labeled data for training often proves inadequate for formulating intra-class compactness and inter-class discrepancy of pseudo labels. To address these issues, we propose a self-aware and cross-sample prototypical learning method (SCP-Net) to enhance the diversity of prediction in consistency learning by utilizing a broader range of semantic information derived from multiple inputs. Furthermore, we introduce a self-aware consistency learning method that exploits unlabeled data to improve the compactness of pseudo labels within each class. Moreover, a dual loss re-weighting method is integrated into the cross-sample prototypical consistency learning method to improve the reliability and stability of our model. Extensive experiments on ACDC dataset and PROMISE12 dataset validate that SCP-Net outperforms other state-of-the-art semi-supervised segmentation methods and achieves significant performance gains compared to the limited supervised training. Our code will come soon.