---
title: "COVID-19 CT Lung and Infection Segmentation Dataset"
description: "9 papers in the tracked journals link to the Zenodo dataset COVID-19 CT Lung and Infection Segmentation Dataset in their Crossref records, the first in 2020; most of them (4) in 2022."
url: https://scholar4ai.com/code/zenodo-3757476
markdown_url: https://scholar4ai.com/code/zenodo-3757476.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "9 papers in the tracked journals link to the Zenodo dataset COVID-19 CT Lung and Infection Segmentation Dataset in their Crossref records, the first in 2020; most of them (4) in 2022. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# COVID-19 CT Lung and Infection Segmentation Dataset: papers linking to it

Research papers in the 480 tracked journals that link to COVID-19 CT Lung and Infection Segmentation Dataset (https://doi.org/10.5281/zenodo.3757476), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- Zenodo
- Dataset
- 2020
- https://doi.org/10.5281/zenodo.3757476

All repositories: https://scholar4ai.com/code.md

## Key findings

- 9 papers in the tracked journals link to the Zenodo dataset COVID-19 CT Lung and Infection Segmentation Dataset in their Crossref records, the first in 2020; most of them (4) in 2022.
- By category: AI (9); the journals with the most are Applied Intelligence (2), Neural Computing and Applications (2) and Expert Systems with Applications (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links COVID-19 CT Lung and Infection Segmentation Dataset.

## By year

*Papers linking to COVID-19 CT Lung and Infection Segmentation Dataset by publication year*

| Year | Papers |
|---|---:|
| 2020 | 1 |
| 2022 | 4 |
| 2024 | 2 |
| 2025 | 2 |

## Journals

*Journals whose papers link to COVID-19 CT Lung and Infection Segmentation Dataset most*

| Journal | Papers |
|---|---:|
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 2 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 2 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |
| [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 1 |
| [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19](https://doi.org/10.1007/s10489-020-01770-9) (Applied Intelligence, 2020)
  - [MA-Net:Mutex attention network for COVID-19 diagnosis on CT images](https://doi.org/10.1007/s10489-022-03431-5) (Applied Intelligence, 2022) — cites the paper above

## Papers

*Papers linking to COVID-19 CT Lung and Infection Segmentation Dataset, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2020-07-05 | [A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19](https://doi.org/10.1007/s10489-020-01770-9) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Youssoufa Mohamadou, Aminou Halidou, Pascalin Tiam Kapen | 333 | cites it |
| 2022-12-28 | [COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans](https://doi.org/10.1007/s12559-022-10076-6) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Kirti Raj Bhatele, Anand Jha, Devanshu Tiwari et al. | 53 | cites it |
| 2022-01-02 | [Using artificial intelligence technology to fight COVID-19: a review](https://doi.org/10.1007/s10462-021-10106-z) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Yong Peng, Enbin Liu, Shanbi Peng et al. | 45 | cites it |
| 2022-05-02 | [DMDF-Net: Dual multiscale dilated fusion network for accurate segmentation of lesions related to COVID-19 in lung radiographic scans](https://doi.org/10.1016/j.eswa.2022.117360) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Muhammad Owais, Na Rae Baek, Kang Ryoung Park | 26 | cites it |
| 2024-09-23 | [Accurate segmentation of COVID-19 infected regions in lung CT scans with deep learning](https://doi.org/10.1007/s00521-024-10336-6) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | U. Lenin Marksia, C. Yesubai Rubavathi | 6 | cites it |
| 2025-09-28 | [SUPER-Net: Trustworthy image segmentation via uncertainty propagation in encoder-decoder networks](https://doi.org/10.1016/j.patcog.2025.112503) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Giuseppina Carannante, Nidhal Carla Bouaynaya, Dimah Dera et al. | 6 | cites it |
| 2022-04-09 | [MA-Net:Mutex attention network for COVID-19 diagnosis on CT images](https://doi.org/10.1007/s10489-022-03431-5) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Bingbing Zheng, Yu Michael Zhu, Qin Shi et al. | 3 | cites it |
| 2024-12-19 | [Simultaneous segmentation and classification of lung CT scans for COVID-19 diagnosis: a deep multi-task learning perspective](https://doi.org/10.1007/s00521-024-10809-8) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Shirin Kordnoori, Malihe Sabeti, Hamidreza Mostafaei et al. | 2 | cites it |
| 2025-10-15 | [Towards building robust models for unimodal and multimodal medical imaging data](https://doi.org/10.1016/j.inffus.2025.103822) | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | Joy Dhar, Puneet Goyal, Maryam Haghighat et al. | 1 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). COVID-19 CT Lung and Infection Segmentation Dataset: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/zenodo-3757476

```bibtex
@misc{scholar4ai-code-zenodo-3757476,
  author = {{Scholar4AI}},
  title = {COVID-19 CT Lung and Infection Segmentation Dataset: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/zenodo-3757476}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).