---
title: "rsna-pneumonia-detection-challenge: papers citing it"
description: "14 papers in the tracked journals link to the Kaggle dataset rsna-pneumonia-detection-challenge in their Crossref records, the first in 2020; most of them (7) in 2021."
url: https://scholar4ai.com/code/kaggle-competition-rsna-pneumonia-detection-challenge
markdown_url: https://scholar4ai.com/code/kaggle-competition-rsna-pneumonia-detection-challenge.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "14 papers in the tracked journals link to the Kaggle dataset rsna-pneumonia-detection-challenge in their Crossref records, the first in 2020; most of them (7) 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)
---

# rsna-pneumonia-detection-challenge: papers linking to it

Research papers in the 480 tracked journals that link to rsna-pneumonia-detection-challenge (https://www.kaggle.com/competitions/rsna-pneumonia-detection-challenge), 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/competitions/rsna-pneumonia-detection-challenge

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

## Key findings

- 14 papers in the tracked journals link to the Kaggle dataset rsna-pneumonia-detection-challenge in their Crossref records, the first in 2020; most of them (7) in 2021.
- By category: AI (14); the journals with the most are Neural Computing and Applications (4), Nature Machine Intelligence (3) and Applied Intelligence (2).
- 7 of the papers build on one another: a later paper cites an earlier one that also links rsna-pneumonia-detection-challenge.

## By year

*Papers linking to rsna-pneumonia-detection-challenge by publication year*

| Year | Papers |
|---|---:|
| 2020 | 2 |
| 2021 | 7 |
| 2023 | 1 |
| 2024 | 1 |
| 2025 | 1 |
| 2026 | 2 |

## Journals

*Journals whose papers link to rsna-pneumonia-detection-challenge most*

| Journal | Papers |
|---|---:|
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 4 |
| [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 3 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 2 |
| [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 2 |
| [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | 2 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays](https://doi.org/10.1016/j.patcog.2020.107613) (Pattern Recognition, 2020)
  - [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://doi.org/10.1038/s42256-021-00307-0) (Nature Machine Intelligence, 2021) — cites the paper above
    - [GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays](https://doi.org/10.1016/j.patcog.2021.108274) (Pattern Recognition, 2021) — cites the paper above
      - [Review on chest pathogies detection systems using deep learning techniques](https://doi.org/10.1007/s10462-023-10457-9) (Artificial Intelligence Review, 2023) — cites the paper above
    - [AI-driven techniques for thoracic disease classification in chest radiography: A comprehensive review and critical analysis](https://doi.org/10.1007/s00521-026-12457-6) (Neural Computing and Applications, 2026) — cites the paper above
- [Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks](https://doi.org/10.1007/s10489-020-01900-3) (Applied Intelligence, 2020)
  - [Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis](https://doi.org/10.1007/s12559-020-09779-5) (Cognitive Computation, 2021) — cites the paper above

## Papers

*Papers linking to rsna-pneumonia-detection-challenge, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2021-03-15 | [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://doi.org/10.1038/s42256-021-00307-0) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Michael Roberts, Derek Driggs, Matthew Thorpe et al. | 1,045 | cites it |
| 2021-05-31 | [AI for radiographic COVID-19 detection selects shortcuts over signal](https://doi.org/10.1038/s42256-021-00338-7) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Alex J. DeGrave, Joseph D. Janizek, Su‐In Lee | 631 | cites it |
| 2020-10-17 | [Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks](https://doi.org/10.1007/s10489-020-01900-3) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Narinder Singh Punn, Sonali Agarwal | 218 | cites it |
| 2020-08-26 | [Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays](https://doi.org/10.1016/j.patcog.2020.107613) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Zheng Wang, Ying Xiao, Yong Li et al. | 186 | cites it |
| 2021-01-08 | [COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays](https://doi.org/10.1007/s00521-020-05636-6) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Rajeev Kumar Singh, Rohan Pandey, Rishie Nandhan Babu | 110 | cites it |
| 2025-01-13 | [Exploring scalable medical image encoders beyond text supervision](https://doi.org/10.1038/s42256-024-00965-w) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Fernando Pérez‐García, Harshita Sharma, Sam Bond-Taylor et al. | 91 | cites it |
| 2021-02-02 | [Corona-Nidaan: lightweight deep convolutional neural network for chest X-Ray based COVID-19 infection detection](https://doi.org/10.1007/s10489-020-01978-9) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Mainak Chakraborty, Sunita Dhavale, Jitendra Ingole | 56 | cites it |
| 2024-02-17 | [TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images](https://doi.org/10.1007/s12559-024-10259-3) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Tawsifur Rahman, Amith Abdullah Khandakar, Ashiqur Rahman et al. | 51 | cites it |
| 2021-03-02 | [Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis](https://doi.org/10.1007/s12559-020-09779-5) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Sejuti Rahman, Sujan Chandra Sarker, Md Abdullah Al Miraj et al. | 48 | cites it |
| 2021-02-25 | [Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks](https://doi.org/10.1007/s00521-020-05641-9) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Kapal Dev, Sunder Ali Khowaja, Ankur Singh Bist et al. | 37 | cites it |
| 2021-08-26 | [GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays](https://doi.org/10.1016/j.patcog.2021.108274) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Angelica I. Avilés-Rivero, Philip Sellars, Carola‐Bibiane Schönlieb et al. | 36 | cites it |
| 2023-03-20 | [Review on chest pathogies detection systems using deep learning techniques](https://doi.org/10.1007/s10462-023-10457-9) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Arshia Rehman, Ahmad Khan, Gohar Fatima et al. | 25 | cites it |
| 2026-02-01 | [Automatic semantic segmentation in chest X-ray images using deep learning approaches: a literature review](https://doi.org/10.1007/s00521-025-11743-z) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Omar Abueed, Priyank Thakkar, Wafa’ H. AlAlaween et al. | 2 | cites it |
| 2026-09-01 | [AI-driven techniques for thoracic disease classification in chest radiography: A comprehensive review and critical analysis](https://doi.org/10.1007/s00521-026-12457-6) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Yosra Didi, Ahlem Walha, Ali Wali | 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). rsna-pneumonia-detection-challenge: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-competition-rsna-pneumonia-detection-challenge

```bibtex
@misc{scholar4ai-code-kaggle-competition-rsna-pneumonia-detection-challenge,
  author = {{Scholar4AI}},
  title = {rsna-pneumonia-detection-challenge: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-competition-rsna-pneumonia-detection-challenge}},
  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/).