# CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging

arXiv: https://arxiv.org/abs/2204.07344 · HTML: https://mlsanity.com/paper/2204.07344/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 41 references that are in the map.
- First posted on arXiv 15 Apr 2022.

## Builds on (most cited first, 30 of 30)

- [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)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [What is being transferred in transfer learning?](https://mlsanity.com/paper/2008.11687/) (2020)
- [Big Self-Supervised Models Advance Medical Image Classification](https://mlsanity.com/paper/2101.05224/) (2021)
- [Contrastive learning of global and local features for medical image segmentation with limited annotations](https://mlsanity.com/paper/2006.10511/) (2020)
- [Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/2011.10043/) (2020)
- [DetCo: Unsupervised Contrastive Learning for Object Detection](https://mlsanity.com/paper/2102.04803/) (2021)
- [Intriguing Properties of Contrastive Losses](https://mlsanity.com/paper/2011.02803/) (2020)
- [What makes instance discrimination good for transfer learning?](https://mlsanity.com/paper/2006.06606/) (2020)
- [A Broad Study on the Transferability of Visual Representations with Contrastive Learning](https://mlsanity.com/paper/2103.13517/) (2021)
- [A critical analysis of self-supervision, or what we can learn from a single image](https://mlsanity.com/paper/1904.13132/) (2019)
- [Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations](https://mlsanity.com/paper/2007.07423/) (2020)
- [Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning](https://mlsanity.com/paper/2102.10680/) (2021)
- [Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts](https://mlsanity.com/paper/2109.04379/) (2021)
- [Joint Generative and Contrastive Learning for Unsupervised Person Re-identification](https://mlsanity.com/paper/2012.09071/) (2020)
- [Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations](https://mlsanity.com/paper/2106.05967/) (2021)
- [Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration](https://mlsanity.com/paper/2007.06959/) (2020)
- [Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning](https://mlsanity.com/paper/2110.14805/) (2021)

## Built on by (most cited first, 18 of 18)

- [Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification](https://mlsanity.com/paper/2210.12843/) (2022)
- [DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis](https://mlsanity.com/paper/2204.10437/) (2022)
- [Robust and Efficient Medical Imaging with Self-Supervision](https://mlsanity.com/paper/2205.09723/) (2022)
- [Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning](https://mlsanity.com/paper/2311.17597/) (2023)
- [Causal Evaluation of Language Models](https://mlsanity.com/paper/2405.00622/) (2024)
- [Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability, Composability, and Decomposability from Anatomy via Self-Supervision](https://mlsanity.com/paper/2404.15672/) (2024)
- [Towards Foundation Models Learned from Anatomy in Medical Imaging via Self-Supervision](https://mlsanity.com/paper/2309.15358/) (2023)
- [A Review of Predictive and Contrastive Self-supervised Learning for Medical Images](https://mlsanity.com/paper/2302.05043/) (2023)
- [Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation](https://mlsanity.com/paper/2310.01680/) (2023)
- [Revisiting Fine-Tuning Strategies for Self-supervised Medical Imaging Analysis](https://mlsanity.com/paper/2307.10915/) (2023)
- [Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training](https://mlsanity.com/paper/2402.11497/) (2024)
- [CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning](https://mlsanity.com/paper/2408.04262/) (2024)
- [A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Biomedical Microscopy Images](https://mlsanity.com/paper/2211.14715/) (2022)
- [Joint Debiased Representation and Image Clustering Learning with Self-Supervision](https://mlsanity.com/paper/2209.06941/) (2022)
- [Dive into Self-Supervised Learning for Medical Image Analysis: Data, Models and Tasks](https://mlsanity.com/paper/2209.12157/) (2022)
- [Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson's Disease](https://mlsanity.com/paper/2301.08141/) (2023)
- [FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation](https://mlsanity.com/paper/2311.13069/) (2023)
- [Coordinative Learning with Ordinal and Relational Priors for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2511.11276/) (2025)

## Abstract (arXiv)

Recently, self-supervised instance discrimination methods have achieved significant success in learning visual representations from unlabeled photographic images. However, given the marked differences between photographic and medical images, the efficacy of instance-based objectives, focusing on learning the most discriminative global features in the image (i.e., wheels in bicycle), remains unknown in medical imaging. Our preliminary analysis showed that high global similarity of medical images in terms of anatomy hampers instance discrimination methods for capturing a set of distinct features, negatively impacting their performance on medical downstream tasks. To alleviate this limitation, we have developed a simple yet effective self-supervised framework, called Context-Aware instance Discrimination (CAiD). CAiD aims to improve instance discrimination learning by providing finer and more discriminative information encoded from a diverse local context of unlabeled medical images. We conduct a systematic analysis to investigate the utility of the learned features from a three-pronged perspective: (i) generalizability and transferability, (ii) separability in the embedding space, and (iii) reusability. Our extensive experiments demonstrate that CAiD (1) enriches representations learned from existing instance discrimination methods; (2) delivers more discriminative features by adequately capturing finer contextual information from individual medial images; and (3) improves reusability of low/mid-level features compared to standard instance discriminative methods. As open science, all codes and pre-trained models are available on our GitHub page: https://github.com/JLiangLab/CAiD.