# IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation

arXiv: https://arxiv.org/abs/2210.15075 · HTML: https://mlsanity.com/paper/2210.15075/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 32 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [Dense Contrastive Learning for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2011.09157/) (2020)
- [Contrastive learning of global and local features for medical image segmentation with limited annotations](https://mlsanity.com/paper/2006.10511/) (2020)
- [PseudoSeg: Designing Pseudo Labels for Semantic Segmentation](https://mlsanity.com/paper/2010.09713/) (2020)
- [3D Self-Supervised Methods for Medical Imaging](https://mlsanity.com/paper/2006.03829/) (2020)
- [Mutual Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2109.09960/) (2021)
- [Self-supervised learning methods and applications in medical imaging analysis: A survey](https://mlsanity.com/paper/2109.08685/) (2021)
- [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)
- [A Survey on Biomedical Image Captioning](https://mlsanity.com/paper/1905.13302/) (2019)
- [Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI](https://mlsanity.com/paper/2209.00123/) (2022)
- [MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation](https://mlsanity.com/paper/2203.14341/) (2022)
- [SWIS: Self-Supervised Representation Learning For Writer Independent Offline Signature Verification](https://mlsanity.com/paper/2202.13078/) (2022)

## Built on by (most cited first, 5 of 5)

- [Semi-supervised Domain Adaptive Medical Image Segmentation through Consistency Regularized Disentangled Contrastive Learning](https://mlsanity.com/paper/2307.02798/) (2023)
- [Multiscale Progressive Text Prompt Network for Medical Image Segmentation](https://mlsanity.com/paper/2307.00174/) (2023)
- [Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI](https://mlsanity.com/paper/2408.05803/) (2024)
- [An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis](https://mlsanity.com/paper/2303.05556/) (2023)
- [Repurposing SAM for User-Defined Semantics Aware Segmentation](https://mlsanity.com/paper/2312.02420/) (2023)

## Abstract (arXiv)

Due to the scarcity of labeled data, Contrastive Self-Supervised Learning (SSL) frameworks have lately shown great potential in several medical image analysis tasks. However, the existing contrastive mechanisms are sub-optimal for dense pixel-level segmentation tasks due to their inability to mine local features. To this end, we extend the concept of metric learning to the segmentation task, using a dense (dis)similarity learning for pre-training a deep encoder network, and employing a semi-supervised paradigm to fine-tune for the downstream task. Specifically, we propose a simple convolutional projection head for obtaining dense pixel-level features, and a new contrastive loss to utilize these dense projections thereby improving the local representations. A bidirectional consistency regularization mechanism involving two-stream model training is devised for the downstream task. Upon comparison, our IDEAL method outperforms the SoTA methods by fair margins on cardiac MRI segmentation. Code available: https://github.com/hritam-98/IDEAL-ICASSP23