# Positional Contrastive Learning for Volumetric Medical Image Segmentation

arXiv: https://arxiv.org/abs/2106.09157 · HTML: https://mlsanity.com/paper/2106.09157/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 16 Jun 2021.

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

- [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)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Contrastive learning of global and local features for medical image segmentation with limited annotations](https://mlsanity.com/paper/2006.10511/) (2020)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound](https://mlsanity.com/paper/2008.06607/) (2020)
- [ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation](https://mlsanity.com/paper/2007.09455/) (2020)
- [MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation](https://mlsanity.com/paper/1909.06726/) (2019)
- [Contrastive Rendering for Ultrasound Image Segmentation](https://mlsanity.com/paper/2010.04928/) (2020)

## Built on by (most cited first, 30 of 56)

- [How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images](https://mlsanity.com/paper/2306.13731/) (2023)
- [Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation](https://mlsanity.com/paper/2109.07407/) (2021)
- [Dissecting Self-Supervised Learning Methods for Surgical Computer Vision](https://mlsanity.com/paper/2207.00449/) (2022)
- [Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective](https://mlsanity.com/paper/2302.01735/) (2023)
- [Federated Contrastive Learning for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2204.10983/) (2022)
- [Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels](https://mlsanity.com/paper/2107.13741/) (2021)
- [Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels](https://mlsanity.com/paper/2209.13476/) (2022)
- [Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation](https://mlsanity.com/paper/2203.15177/) (2022)
- [Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2112.02508/) (2021)
- [Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning](https://mlsanity.com/paper/2202.07470/) (2022)
- [Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis](https://mlsanity.com/paper/2206.04281/) (2022)
- [Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2412.02621/) (2024)
- [Distributed Contrastive Learning for Medical Image Segmentation](https://mlsanity.com/paper/2208.03808/) (2022)
- [Metadata-enhanced contrastive learning from retinal optical coherence tomography images](https://mlsanity.com/paper/2208.02529/) (2022)
- [Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI](https://mlsanity.com/paper/2209.00123/) (2022)
- [BT-Unet: A self-supervised learning framework for biomedical image segmentation using Barlow Twins with U-Net models](https://mlsanity.com/paper/2112.03916/) (2021)
- [Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation](https://mlsanity.com/paper/2206.08158/) (2022)
- [ViT-AE++: Improving Vision Transformer Autoencoder for Self-supervised Medical Image Representations](https://mlsanity.com/paper/2301.07382/) (2023)
- [Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures](https://mlsanity.com/paper/2405.04700/) (2024)
- [Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation](https://mlsanity.com/paper/2112.02743/) (2021)
- [A Review of Predictive and Contrastive Self-supervised Learning for Medical Images](https://mlsanity.com/paper/2302.05043/) (2023)
- [Mini-Batch Optimization of Contrastive Loss](https://mlsanity.com/paper/2307.05906/) (2023)
- [MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation](https://mlsanity.com/paper/2207.01883/) (2022)
- [Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation](https://mlsanity.com/paper/2310.01680/) (2023)
- [Self-Supervised Alignment Learning for Medical Image Segmentation](https://mlsanity.com/paper/2406.15699/) (2024)
- [MouseGAN++: Unsupervised Disentanglement and Contrastive Representation for Multiple MRI Modalities Synthesis and Structural Segmentation of Mouse Brain](https://mlsanity.com/paper/2212.01825/) (2022)
- [Contrastive Learning with Synthetic Positives](https://mlsanity.com/paper/2408.16965/) (2024)
- [IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2210.15075/) (2022)
- [Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Images](https://mlsanity.com/paper/2211.08643/) (2022)
- [GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels](https://mlsanity.com/paper/2309.08888/) (2023)

## Abstract (arXiv)

The success of deep learning heavily depends on the availability of large labeled training sets. However, it is hard to get large labeled datasets in medical image domain because of the strict privacy concern and costly labeling efforts. Contrastive learning, an unsupervised learning technique, has been proved powerful in learning image-level representations from unlabeled data. The learned encoder can then be transferred or fine-tuned to improve the performance of downstream tasks with limited labels. A critical step in contrastive learning is the generation of contrastive data pairs, which is relatively simple for natural image classification but quite challenging for medical image segmentation due to the existence of the same tissue or organ across the dataset. As a result, when applied to medical image segmentation, most state-of-the-art contrastive learning frameworks inevitably introduce a lot of false-negative pairs and result in degraded segmentation quality. To address this issue, we propose a novel positional contrastive learning (PCL) framework to generate contrastive data pairs by leveraging the position information in volumetric medical images. Experimental results on CT and MRI datasets demonstrate that the proposed PCL method can substantially improve the segmentation performance compared to existing methods in both semi-supervised setting and transfer learning setting.