Labeled Optical Coherence Tomography (OCT) for Classification: papers linking to it

Mendeley Data · Dataset · 2017 · data.mendeley.com/datasets/rscbjbr9sj

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to Labeled Optical Coherence Tomography (OCT) for Classification (https://data.mendeley.com/datasets/rscbjbr9sj), 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#

10 papers in the tracked journals link to the Mendeley Data dataset Labeled Optical Coherence Tomography (OCT) for Classification in their Crossref records, the first in 2021; most of them (4) in 2021.
By category: AI (10); the journals with the most are Neural Computing and Applications (2), Cognitive Computation (2) and Knowledge-Based Systems (1).

By year#

20212025

Papers linking to Labeled Optical Coherence Tomography (OCT) for Classification by publication year
Year	Papers
2021	4
2022	2
2023	2
2024	1
2025	1

Journals#

Journals whose papers link to Labeled Optical Coherence Tomography (OCT) for Classification most
Journal	Papers
Neural Computing and Applications	2
Cognitive Computation	2
Knowledge-Based Systems	1
Applied Intelligence	1
IEEE Transactions on Neural Networks and Learning Systems	1

Papers linking to Labeled Optical Coherence Tomography (OCT) for Classification, most cited first
Published	Title	Journal	Authors	Citations	Link
2021-05-24	End-to-end privacy preserving deep learning on multi-institutional medical imaging	Nature Machine Intelligence	Georgios A. Kaissis, Alexander Ziller, Jonathan Passerat‐Palmbach et al.	506	cites it
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
2021-01-06	COV19-CNNet and COV19-ResNet: Diagnostic Inference Engines for Early Detection of COVID-19	Cognitive Computation	Aytürk Keleş, Mustafa Berk Keles, Mustafa Berk Keles et al.	73	cites it
2021-03-02	Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis	Cognitive Computation	Sejuti Rahman, Sujan Chandra Sarker, Md Abdullah Al Miraj et al.	48	cites it
2024-03-01	A novel lightweight CNN for chest X-ray-based lung disease identification on heterogeneous embedded system	Applied Intelligence	Theodora Sanida, Minas Dasygenis	31	cites it
2022-01-13	Challenges for ocular disease identification in the era of artificial intelligence	Neural Computing and Applications	Neha Gour, M. Tanveer, Pritee Khanna	30	cites it
2023-06-28	An Adaptive Deep Metric Learning Loss Function for Class-Imbalance Learning via Intraclass Diversity and Interclass Distillation	IEEE Transactions on Neural Networks and Learning Systems	Jie Du, Xiaoci Zhang, Peng Liu et al.	27	cites it
2023-12-28	Federated Learning for Distribution Skewed Data Using Sample Weights	IEEE Transactions on Artificial Intelligence	Hung T. Nguyen, Pei-Yuan Wu, J. Morris Chang	16	cites it
2022-11-02	Utilization of big data classification models in digitally enhanced optical coherence tomography for medical diagnostics	Neural Computing and Applications	Priti Bansal, Nipun Harjai, Mohammad Saif et al.	15	cites it
2025-12-07	Which domain fits best? domain similarity measures for two-step heterogeneous transfer learning for early laryngeal cancer diagnosis	Knowledge-Based Systems	Xinyi Fang, Yuqi Luo, Kei Long Wong 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). Labeled Optical Coherence Tomography (OCT) for Classification: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/mendeley-rscbjbr9sj

@misc{scholar4ai-code-mendeley-rscbjbr9sj,
  author = {{Scholar4AI}},
  title = {Labeled Optical Coherence Tomography (OCT) for Classification: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/mendeley-rscbjbr9sj}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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