CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data: papers linking to it

Zenodo · Dataset · 2019 · doi.org/10.5281/zenodo.3362844

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data (https://doi.org/10.5281/zenodo.3362844), 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#

3 papers in the tracked journals link to the Zenodo dataset CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data in their Crossref records, the first in 2024; most of them (2) in 2026.
By category: AI (3); the journals with the most are Expert Systems with Applications (1), Applied Intelligence (1) and Neural Computing and Applications (1).

By year#

20242026

Papers linking to CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data by publication year
Year	Papers
2024	1
2026	2

Journals#

Journals whose papers link to CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data most
Journal	Papers
Expert Systems with Applications	1
Applied Intelligence	1
Neural Computing and Applications	1

Papers linking to CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data, most cited first
Published	Title	Journal	Authors	Citations	Link
2024-04-30	Deep learning-assisted medical image compression challenges and opportunities: systematic review	Neural Computing and Applications	Nour El Houda Bourai, Hayet Farida Merouani, Akila Djebbar	25	cites it
2026-03-02	Unsupervised deformable image registration with local-global attention and image decomposition	Expert Systems with Applications	Zhengyong Huang, Xingwen Sun, Xuting Chang et al.	2	cites it
2026-10-01	A needle path planning method for liver tumor radiofrequency ablation based on medical image segmentation deep neural networks	Applied Intelligence	Dan Liu, Tianjiao Duo	0	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). CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/zenodo-3362844

@misc{scholar4ai-code-zenodo-3362844,
  author = {{Scholar4AI}},
  title = {CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/zenodo-3362844}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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