# Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation

arXiv: https://arxiv.org/abs/2004.12537 · HTML: https://mlsanity.com/paper/2004.12537/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 30 of 53)

- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [Large-Vocabulary Segmentation for Medical Images with Text Prompts](https://mlsanity.com/paper/2312.17183/) (2023)
- [Segment Anything in Medical Images and Videos: Benchmark and Deployment](https://mlsanity.com/paper/2408.03322/) (2024)
- [COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning](https://mlsanity.com/paper/2101.07433/) (2021)
- [Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2112.02508/) (2021)
- [Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans](https://mlsanity.com/paper/2101.05442/) (2021)
- [COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs](https://mlsanity.com/paper/2209.10963/) (2022)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?](https://mlsanity.com/paper/2101.00232/) (2021)
- [Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification](https://mlsanity.com/paper/2211.14559/) (2022)
- [Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis](https://mlsanity.com/paper/2502.15204/) (2025)
- [STPNet: Scale-aware Text Prompt Network for Medical Image Segmentation](https://mlsanity.com/paper/2504.01561/) (2025)
- [Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A review](https://mlsanity.com/paper/2110.14910/) (2021)
- [Medical Image Segmentation with Domain Adaptation: A Survey](https://mlsanity.com/paper/2311.01702/) (2023)
- [SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts](https://mlsanity.com/paper/2403.13258/) (2024)
- [DB-SAM: Delving into High Quality Universal Medical Image Segmentation](https://mlsanity.com/paper/2410.04172/) (2024)
- [Feasibility Study of Multi-Site Split Learning for Privacy-Preserving Medical Systems under Data Imbalance Constraints in COVID-19, X-Ray, and Cholesterol Dataset](https://mlsanity.com/paper/2202.10456/) (2022)
- [Deep Learning in Single-Cell Analysis](https://mlsanity.com/paper/2210.12385/) (2022)
- [COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Images](https://mlsanity.com/paper/2302.02619/) (2023)
- [LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models](https://mlsanity.com/paper/2409.19540/) (2024)
- [Lung-DDPM+: Efficient Thoracic CT Image Synthesis using Diffusion Probabilistic Model](https://mlsanity.com/paper/2508.09327/) (2025)
- [Automated lung segmentation from CT images of normal and COVID-19 pneumonia patients](https://mlsanity.com/paper/2104.02042/) (2021)
- [DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problem](https://mlsanity.com/paper/2303.05912/) (2023)
- [A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labels](https://mlsanity.com/paper/2312.15972/) (2023)
- [Is Visual in-Context Learning for Compositional Medical Tasks within Reach?](https://mlsanity.com/paper/2507.00868/) (2025)
- [D-TrAttUnet: Dual-Decoder Transformer-Based Attention Unet Architecture for Binary and Multi-classes Covid-19 Infection Segmentation](https://mlsanity.com/paper/2303.15576/) (2023)
- [Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach](https://mlsanity.com/paper/2506.03238/) (2025)
- [MedROV: Towards Real-Time Open-Vocabulary Detection Across Diverse Medical Imaging Modalities](https://mlsanity.com/paper/2511.20650/) (2025)
- [3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware](https://mlsanity.com/paper/2101.09976/) (2021)
- [COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT Images](https://mlsanity.com/paper/2206.03043/) (2022)

## Abstract (arXiv)

Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID-19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. Methods: To promote the development of data-efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID-19 cases, which contain current active research areas, e.g., few-shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. Results: Based on the state-of-the-art network, we provide more than 40 pre-trained baseline models, which not only serve as out-of-the-box segmentation tools but also save computational time for researchers who are interested in COVID-19 lung and infection segmentation. We achieve average Dice Similarity Coefficient (DSC) scores of 97.3%, 97.7%, and 67.3% and average Normalized Surface Dice (NSD) scores of 90.6%, 91.4%, and 70.0% for left lung, right lung, and infection, respectively. Conclusions: To the best of our knowledge, this work presents the first data-efficient learning benchmark for medical image segmentation and the largest number of pre-trained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID-19 CT segmentation with limited data.