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

Kaggle · www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation

← Code and data repositories cited in research papers

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.

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#

20212026

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	2
Expert Systems with Applications	1
Applied Intelligence	1

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	Expert Systems with Applications	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	Applied Intelligence	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	Knowledge-Based Systems	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	Knowledge-Based Systems	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

@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}
}

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