---
title: "kmader/finding-lungs-in-ct-data: papers citing it"
description: "5 papers in the tracked journals link to the Kaggle dataset kmader/finding-lungs-in-ct-data in their Crossref records, the first in 2021; most of them (2) in 2021."
url: https://scholar4ai.com/code/kaggle-kmader-finding-lungs-in-ct-data
markdown_url: https://scholar4ai.com/code/kaggle-kmader-finding-lungs-in-ct-data.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "5 papers in the tracked journals link to the Kaggle dataset kmader/finding-lungs-in-ct-data in their Crossref records, the first in 2021; most of them (2) in 2021. (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)
---

# kmader/finding-lungs-in-ct-data: papers linking to it

Research papers in the 480 tracked journals that link to kmader/finding-lungs-in-ct-data (https://www.kaggle.com/datasets/kmader/finding-lungs-in-ct-data), 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.

- Kaggle
- https://www.kaggle.com/datasets/kmader/finding-lungs-in-ct-data

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

## Key findings

- 5 papers in the tracked journals link to the Kaggle dataset kmader/finding-lungs-in-ct-data in their Crossref records, the first in 2021; most of them (2) in 2021.
- By category: AI (5); the journals with the most are Neural Computing and Applications (2), Knowledge-Based Systems (1) and Applied Intelligence (1).

## By year

*Papers linking to kmader/finding-lungs-in-ct-data by publication year*

| Year | Papers |
|---|---:|
| 2021 | 2 |
| 2022 | 1 |
| 2023 | 1 |
| 2025 | 1 |

## Journals

*Journals whose papers link to kmader/finding-lungs-in-ct-data most*

| Journal | Papers |
|---|---:|
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 2 |
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 1 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |
| [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 1 |

## Papers

*Papers linking to kmader/finding-lungs-in-ct-data, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2021-06-01 | [Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images](https://doi.org/10.1016/j.patcog.2021.108071) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Zhao Chen, Yan Xu, Zhuo He et al. | 110 | cites it |
| 2022-04-25 | [Semantic segmentation in medical images through transfused convolution and transformer networks](https://doi.org/10.1007/s10489-022-03642-w) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Tashvik Dhamija, Anunay Gupta, Shreyansh Gupta et al. | 74 | cites it |
| 2023-03-26 | [DoubleU-NetPlus: a novel attention and context-guided dual U-Net with multi-scale residual feature fusion network for semantic segmentation of medical images](https://doi.org/10.1007/s00521-023-08493-1) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Md. Rayhan Ahmed, Md. Rayhan Ahmed, Adnan Ferdous Ashrafi et al. | 34 | cites it |
| 2021-03-18 | [Medical image segmentation based on active fusion-transduction of multi-stream features](https://doi.org/10.1016/j.knosys.2021.106950) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Yucheng Shu, Jing Zhang, Bin Xiao et al. | 28 | cites it |
| 2025-01-06 | [Masked hybrid attention with Laplacian query fusion and tripartite sequence matching for medical image segmentation](https://doi.org/10.1007/s00521-024-10934-4) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Favour Ekong, Yongbin Yu, Rutherford Agbeshi Patamia et al. | 2 | 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). kmader/finding-lungs-in-ct-data: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-kmader-finding-lungs-in-ct-data

```bibtex
@misc{scholar4ai-code-kaggle-kmader-finding-lungs-in-ct-data,
  author = {{Scholar4AI}},
  title = {kmader/finding-lungs-in-ct-data: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-kmader-finding-lungs-in-ct-data}},
  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/).