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

Zenodo · Dataset · 2020 · doi.org/10.5281/zenodo.3757476

← Code and data repositories cited in research papers

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.

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#

20202025

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	2
Neural Computing and Applications	2
Expert Systems with Applications	1
Artificial Intelligence Review	1
Information Fusion	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19 Applied Intelligence, 2020
- MA-Net:Mutex attention network for COVID-19 diagnosis on CT images Applied Intelligence, 2022 — cites the paper above

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	Applied Intelligence	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	Cognitive Computation	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	Artificial Intelligence Review	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	Expert Systems with Applications	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	Neural Computing and Applications	U. Lenin Marksia, C. Yesubai Rubavathi	6	cites it
2025-09-28	SUPER-Net: Trustworthy image segmentation via uncertainty propagation in encoder-decoder networks	Pattern Recognition	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	Applied Intelligence	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	Neural Computing and Applications	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	Information Fusion	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

@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}
}

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