---
title: "diabetic-retinopathy-detection: papers citing this dataset"
description: "19 papers in the tracked journals link to the Kaggle dataset diabetic-retinopathy-detection in their Crossref records, the first in 2019; most of them (6) in 2025."
url: https://scholar4ai.com/code/kaggle-competition-diabetic-retinopathy-detection
markdown_url: https://scholar4ai.com/code/kaggle-competition-diabetic-retinopathy-detection.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "19 papers in the tracked journals link to the Kaggle dataset diabetic-retinopathy-detection in their Crossref records, the first in 2019; most of them (6) in 2025. (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)
---

# diabetic-retinopathy-detection: papers linking to it

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

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

## Key findings

- 19 papers in the tracked journals link to the Kaggle dataset diabetic-retinopathy-detection in their Crossref records, the first in 2019; most of them (6) in 2025.
- By category: AI (18) and Statistics (1); the journals with the most are Knowledge-Based Systems (4), Pattern Recognition (4) and Artificial Intelligence Review (2).
- 4 of the papers build on one another: a later paper cites an earlier one that also links diabetic-retinopathy-detection.

## By year

*Papers linking to diabetic-retinopathy-detection by publication year*

| Year | Papers |
|---|---:|
| 2019 | 1 |
| 2020 | 2 |
| 2021 | 1 |
| 2022 | 2 |
| 2023 | 5 |
| 2024 | 1 |
| 2025 | 6 |
| 2026 | 1 |

## Journals

*Journals whose papers link to diabetic-retinopathy-detection most*

| Journal | Papers |
|---|---:|
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 4 |
| [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 4 |
| [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 2 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 2 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Understanding adversarial attacks on deep learning based medical image analysis systems](https://doi.org/10.1016/j.patcog.2020.107332) (Pattern Recognition, 2020)
  - [Enhancing the adversarial robustness in medical image classification: exploring adversarial machine learning with vision transformers-based models](https://doi.org/10.1007/s00521-024-10516-4) (Neural Computing and Applications, 2024) — cites the paper above
- [Diabetic retinopathy identification using parallel convolutional neural network based feature extractor and ELM classifier](https://doi.org/10.1016/j.eswa.2023.119557) (Expert Systems with Applications, 2023)
  - [Two-stage framework for diabetic retinopathy diagnosis and disease stage screening with ensemble learning](https://doi.org/10.1016/j.eswa.2023.120206) (Expert Systems with Applications, 2023) — cites the paper above

## Papers

*Papers linking to diabetic-retinopathy-detection, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2020-05-01 | [Understanding adversarial attacks on deep learning based medical image analysis systems](https://doi.org/10.1016/j.patcog.2020.107332) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Xingjun Ma, Yuhao Niu, Lin Gu et al. | 548 | cites it |
| 2019-07-28 | [Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation](https://doi.org/10.1016/j.csda.2019.106816) | [Computational Statistics & Data Analysis](https://scholar4ai.com/journals/computational-statistics-data-analysis.md) | Yongchan Kwon, Joong‐Ho Won, Beom Joon Kim et al. | 423 | cites it |
| 2023-01-13 | [Diabetic retinopathy identification using parallel convolutional neural network based feature extractor and ELM classifier](https://doi.org/10.1016/j.eswa.2023.119557) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Md. Nahiduzzaman, Md. Robiul Islam, Md. Omaer Faruq Goni et al. | 142 | cites it |
| 2022-01-25 | [Predicting myocardial infarction through retinal scans and minimal personal information](https://doi.org/10.1038/s42256-021-00427-7) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Andres Diaz‐Pinto, Nishant Ravikumar, Rahman Attar et al. | 105 | cites it |
| 2023-07-20 | [Tackling class imbalance in computer vision: a contemporary review](https://doi.org/10.1007/s10462-023-10557-6) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Manisha Saini, Seba Susan | 68 | cites it |
| 2022-01-31 | [Visual privacy attacks and defenses in deep learning: a survey](https://doi.org/10.1007/s10462-021-10123-y) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Guangsheng Zhang, Bo Liu, Tianqing Zhu et al. | 51 | cites it |
| 2023-04-20 | [Two-stage framework for diabetic retinopathy diagnosis and disease stage screening with ensemble learning](https://doi.org/10.1016/j.eswa.2023.120206) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Mohammad H. Alshayeji, Sa’ed Abed, Silpa ChandraBhasi Sindhu | 23 | cites it |
| 2020-06-18 | [Efficient sampling-based energy function evaluation for ensemble optimization using simulated annealing](https://doi.org/10.1016/j.patcog.2020.107510) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | János Attila Tóth, Henrietta Tomán, András Hajdú | 18 | cites it |
| 2023-10-16 | [SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels](https://doi.org/10.1007/s11263-023-01918-3) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Hengyuan Zhao, Pichao Wang, Yuyang Zhao et al. | 17 | cites it |
| 2021-02-01 | [Automatic analysis of artistic paintings using information-based measures](https://doi.org/10.1016/j.patcog.2021.107864) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Jorge Miguel Silva, Diogo Pratas, Rui Antunes et al. | 15 | cites it |
| 2023-11-09 | [Hyperparameter optimization of pre-trained convolutional neural networks using adolescent identity search algorithm](https://doi.org/10.1007/s00521-023-09121-8) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Ebubekir Akkuş, Ufuk Bal, Fatma Önay Koçoğlu et al. | 10 | cites it |
| 2024-11-29 | [Enhancing the adversarial robustness in medical image classification: exploring adversarial machine learning with vision transformers-based models](https://doi.org/10.1007/s00521-024-10516-4) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Elif Kanca, Selen Ayas, Elif Baykal Kablan et al. | 8 | cites it |
| 2025-08-30 | [Visual Instruction Tuning towards General-Purpose Multimodal Large Language Model: A Survey](https://doi.org/10.1007/s11263-025-02572-7) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Jiaxing Huang, Jingyi Zhang, Kai Yong Jiang et al. | 8 | cites it |
| 2025-10-05 | [Secure and incentivized federated learning for resilient telemedicine diagnostics](https://doi.org/10.1016/j.knosys.2025.114622) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Omar Dib | 7 | cites it |
| 2025-07-07 | [Bi-branch bidirectional coupled interaction fusion network for multi-retinal diseases diagnosis](https://doi.org/10.1016/j.knosys.2025.114069) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Shaobin Chen, Tao Tan, Xinyu Zhao et al. | 2 | cites it |
| 2025-11-11 | [Revisiting the representation learning in long-tailed medical image classification](https://doi.org/10.1016/j.patcog.2025.112683) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Mianzimei Yang, Zhipeng Zhou, Wei Gong | 2 | cites it |
| 2025-12-07 | [Which domain fits best? domain similarity measures for two-step heterogeneous transfer learning for early laryngeal cancer diagnosis](https://doi.org/10.1016/j.knosys.2025.115056) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Xinyi Fang, Yuqi Luo, Kei Long Wong et al. | 2 | cites it |
| 2026-04-15 | [A large-scale multi-view fundus images dataset and benchmark for diabetic retinopathy grading](https://doi.org/10.1016/j.inffus.2026.104363) | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | Xiaoling Luo, Qiaojian Zheng, Chengliang Liu et al. | 1 | cites it |
| 2025-10-06 | [Analysis of model-based ensemble learning for accurate diabetic retinopathy grading](https://doi.org/10.1016/j.knosys.2025.114581) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Huang-Chia Shih, Yuchang Cheng, Chia-Shiang Chen 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). diabetic-retinopathy-detection: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-competition-diabetic-retinopathy-detection

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