# Few-shot Learning for CT Scan based COVID-19 Diagnosis

arXiv: https://arxiv.org/abs/2102.00596 · HTML: https://mlsanity.com/paper/2102.00596/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Unified Deep Supervised Domain Adaptation and Generalization](https://mlsanity.com/paper/1709.10190/) (2017)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)
- [Few-Shot Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.02536/) (2017)
- [Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images](https://mlsanity.com/paper/2006.13276/) (2020)
- [d-SNE: Domain Adaptation using Stochastic Neighborhood Embedding](https://mlsanity.com/paper/1905.12775/) (2019)
- [COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network](https://mlsanity.com/paper/2007.14638/) (2020)
- [Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning](https://mlsanity.com/paper/2005.03227/) (2020)

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

- [Multimodal Parameter-Efficient Few-Shot Class Incremental Learning](https://mlsanity.com/paper/2303.04751/) (2023)
- [Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19](https://mlsanity.com/paper/2311.06258/) (2023)
- [An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis](https://mlsanity.com/paper/2507.08050/) (2025)
- [Enhancing Early Alzheimer Disease Detection through Big Data and Ensemble Few-Shot Learning](https://mlsanity.com/paper/2510.19282/) (2025)

## Abstract (arXiv)

Coronavirus disease 2019 (COVID-19) is a Public Health Emergency of International Concern infecting more than 40 million people across 188 countries and territories. Chest computed tomography (CT) imaging technique benefits from its high diagnostic accuracy and robustness, it has become an indispensable way for COVID-19 mass testing. Recently, deep learning approaches have become an effective tool for automatic screening of medical images, and it is also being considered for COVID-19 diagnosis. However, the high infection risk involved with COVID-19 leads to relative sparseness of collected labeled data limiting the performance of such methodologies. Moreover, accurately labeling CT images require expertise of radiologists making the process expensive and time-consuming. In order to tackle the above issues, we propose a supervised domain adaption based COVID-19 CT diagnostic method which can perform effectively when only a small samples of labeled CT scans are available. To compensate for the sparseness of labeled data, the proposed method utilizes a large amount of synthetic COVID-19 CT images and adjusts the networks from the source domain (synthetic data) to the target domain (real data) with a cross-domain training mechanism. Experimental results show that the proposed method achieves state-of-the-art performance on few-shot COVID-19 CT imaging based diagnostic tasks.