# Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images

arXiv: https://arxiv.org/abs/2006.13276 · HTML: https://mlsanity.com/paper/2006.13276/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 30 of 43)

- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [Self-supervised learning methods and applications in medical imaging analysis: A survey](https://mlsanity.com/paper/2109.08685/) (2021)
- [COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction](https://mlsanity.com/paper/2101.04909/) (2021)
- [Federated Contrastive Learning for Decentralized Unlabeled Medical Images](https://mlsanity.com/paper/2109.07504/) (2021)
- [Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments](https://mlsanity.com/paper/2105.08149/) (2021)
- [C2F-TCN: A Framework for Semi and Fully Supervised Temporal Action Segmentation](https://mlsanity.com/paper/2212.11078/) (2022)
- [FHIST: A Benchmark for Few-shot Classification of Histological Images](https://mlsanity.com/paper/2206.00092/) (2022)
- [Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning](https://mlsanity.com/paper/2110.14805/) (2021)
- [Iterative Contrast-Classify For Semi-supervised Temporal Action Segmentation](https://mlsanity.com/paper/2112.01402/) (2021)
- [Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning](https://mlsanity.com/paper/2202.09059/) (2022)
- [Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging](https://mlsanity.com/paper/2308.06534/) (2023)
- [Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learners](https://mlsanity.com/paper/2205.09048/) (2022)
- [An Evaluation of Self-Supervised Pre-Training for Skin-Lesion Analysis](https://mlsanity.com/paper/2106.09229/) (2021)
- [Meta-learning in healthcare: A survey](https://mlsanity.com/paper/2308.02877/) (2023)
- [Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning](https://mlsanity.com/paper/2112.10982/) (2021)
- [GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing](https://mlsanity.com/paper/2307.14612/) (2023)
- [Few-shot Learning for CT Scan based COVID-19 Diagnosis](https://mlsanity.com/paper/2102.00596/) (2021)
- [Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung Ultrasound](https://mlsanity.com/paper/2201.07357/) (2022)
- [Generative Adversarial U-Net for Domain-free Medical Image Augmentation](https://mlsanity.com/paper/2101.04793/) (2021)
- [Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation](https://mlsanity.com/paper/2106.01596/) (2021)
- [Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images](https://mlsanity.com/paper/2205.15530/) (2022)
- [A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning](https://mlsanity.com/paper/2211.08533/) (2022)
- [PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification](https://mlsanity.com/paper/2404.08915/) (2024)
- [CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning](https://mlsanity.com/paper/2105.11863/) (2021)
- [Exploring the Diversity and Invariance in Yourself for Visual Pre-Training Task](https://mlsanity.com/paper/2106.00537/) (2021)
- [Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited triplets](https://mlsanity.com/paper/2204.07824/) (2022)
- [Unsupervised Feature Clustering Improves Contrastive Representation Learning for Medical Image Segmentation](https://mlsanity.com/paper/2211.08557/) (2022)
- [Few-Shot Histopathology Image Classification: Evaluating State-of-the-Art Methods and Unveiling Performance Insights](https://mlsanity.com/paper/2408.13816/) (2024)
- [Cross-Domain Evaluation of Few-Shot Classification Models: Natural Images vs. Histopathological Images](https://mlsanity.com/paper/2410.09176/) (2024)
- [OWT: A Foundational Organ-Wise Tokenization Framework for Medical Imaging](https://mlsanity.com/paper/2505.04899/) (2025)

## Abstract (arXiv)

The current pandemic, caused by the outbreak of a novel coronavirus (COVID-19) in December 2019, has led to a global emergency that has significantly impacted economies, healthcare systems and personal wellbeing all around the world. Controlling the rapidly evolving disease requires highly sensitive and specific diagnostics. While real-time RT-PCR is the most commonly used, these can take up to 8 hours, and require significant effort from healthcare professionals. As such, there is a critical need for a quick and automatic diagnostic system. Diagnosis from chest CT images is a promising direction. However, current studies are limited by the lack of sufficient training samples, as acquiring annotated CT images is time-consuming. To this end, we propose a new deep learning algorithm for the automated diagnosis of COVID-19, which only requires a few samples for training. Specifically, we use contrastive learning to train an encoder which can capture expressive feature representations on large and publicly available lung datasets and adopt the prototypical network for classification. We validate the efficacy of the proposed model in comparison with other competing methods on two publicly available and annotated COVID-19 CT datasets. Our results demonstrate the superior performance of our model for the accurate diagnosis of COVID-19 based on chest CT images.