# Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation

arXiv: https://arxiv.org/abs/2403.03512 · HTML: https://mlsanity.com/paper/2403.03512/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Contrastive learning of global and local features for medical image segmentation with limited annotations](https://mlsanity.com/paper/2006.10511/) (2020)
- [Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification](https://mlsanity.com/paper/2103.14267/) (2021)
- [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks](https://mlsanity.com/paper/1511.06390/) (2015)
- [Positional Contrastive Learning for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2106.09157/) (2021)
- [DiffDP: Radiotherapy Dose Prediction via a Diffusion Model](https://mlsanity.com/paper/2307.09794/) (2023)
- [Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation](https://mlsanity.com/paper/2112.02743/) (2021)
- [An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2202.00677/) (2022)
- [Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks](https://mlsanity.com/paper/1904.04445/) (2019)
- [ASMFS: Adaptive-Similarity-based Multi-modality Feature Selection for Classification of Alzheimer's Disease](https://mlsanity.com/paper/2010.08190/) (2020)
- [EVIL: Evidential Inference Learning for Trustworthy Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2307.08988/) (2023)

## Built on by (most cited first, 4 of 4)

- [ARANet: Attention-based Residual Adversarial Network with Deep Supervision for Radiotherapy Dose Prediction of Cervical Cancer](https://mlsanity.com/paper/2408.13981/) (2024)
- [Alleviating Class Imbalance in Semi-supervised Multi-organ Segmentation via Balanced Subclass Regularization](https://mlsanity.com/paper/2408.14047/) (2024)
- [Judge Like a Real Doctor: Dual Teacher Sample Consistency Framework for Semi-supervised Medical Image Classification](https://mlsanity.com/paper/2411.03041/) (2024)
- [SPG-CDENet: Spatial Prior-Guided Cross Dual Encoder Network for Multi-Organ Segmentation](https://mlsanity.com/paper/2510.26390/) (2025)

## Abstract (arXiv)

Semi-supervised learning is a sound measure to relieve the strict demand of abundant annotated datasets, especially for challenging multi-organ segmentation . However, most existing SSL methods predict pixels in a single image independently, ignoring the relations among images and categories. In this paper, we propose a two-stage Dual Contrastive Learning Network for semi-supervised MoS, which utilizes global and local contrastive learning to strengthen the relations among images and classes. Concretely, in Stage 1, we develop a similarity-guided global contrastive learning to explore the implicit continuity and similarity among images and learn global context. Then, in Stage 2, we present an organ-aware local contrastive learning to further attract the class representations. To ease the computation burden, we introduce a mask center computation algorithm to compress the category representations for local contrastive learning. Experiments conducted on the public 2017 ACDC dataset and an in-house RC-OARs dataset has demonstrated the superior performance of our method.