---
title: "mateuszbuda/lgg-mri-segmentation: papers citing it"
description: "4 papers in the tracked journals link to the Kaggle dataset mateuszbuda/lgg-mri-segmentation in their Crossref records, the first in 2021; most of them (1) in 2026."
url: https://scholar4ai.com/code/kaggle-mateuszbuda-lgg-mri-segmentation
markdown_url: https://scholar4ai.com/code/kaggle-mateuszbuda-lgg-mri-segmentation.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "4 papers in the tracked journals link to the Kaggle dataset mateuszbuda/lgg-mri-segmentation in their Crossref records, the first in 2021; most of them (1) in 2026. (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)
---

# mateuszbuda/lgg-mri-segmentation: papers linking to it

Research papers in the 480 tracked journals that link to mateuszbuda/lgg-mri-segmentation (https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation), 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/mateuszbuda/lgg-mri-segmentation

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

## Key findings

- 4 papers in the tracked journals link to the Kaggle dataset mateuszbuda/lgg-mri-segmentation in their Crossref records, the first in 2021; most of them (1) in 2026.
- By category: AI (4); the journals with the most are Knowledge-Based Systems (2), Expert Systems with Applications (1) and Applied Intelligence (1).

## By year

*Papers linking to mateuszbuda/lgg-mri-segmentation by publication year*

| Year | Papers |
|---|---:|
| 2021 | 1 |
| 2022 | 1 |
| 2025 | 1 |
| 2026 | 1 |

## Journals

*Journals whose papers link to mateuszbuda/lgg-mri-segmentation most*

| Journal | Papers |
|---|---:|
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 2 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |

## Papers

*Papers linking to mateuszbuda/lgg-mri-segmentation, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2021-02-15 | [Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19](https://doi.org/10.1016/j.eswa.2021.114677) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Plácido L. Vidal, Joaquim de Moura, Jorge Novo et al. | 74 | 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 |
| 2026-02-10 | [SHAPER: SHape-Aware Parameter-Efficient Representation learning for medical image segmentation](https://doi.org/10.1016/j.knosys.2026.115477) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Usma Niyaz, Abhishek Singh Sambyal, Ranjana Roy Chowdhury et al. | 1 | cites it |
| 2025-11-15 | [Transfer learning-based dual-suppressed possibilistic Gustafson-Kessel clustering for imbalanced image segmentation within similar scenes](https://doi.org/10.1016/j.knosys.2025.114880) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Haiyan Yu, Qian Gao, Zihan Yang et al. | 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). mateuszbuda/lgg-mri-segmentation: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-mateuszbuda-lgg-mri-segmentation

```bibtex
@misc{scholar4ai-code-kaggle-mateuszbuda-lgg-mri-segmentation,
  author = {{Scholar4AI}},
  title = {mateuszbuda/lgg-mri-segmentation: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-mateuszbuda-lgg-mri-segmentation}},
  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/).