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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach
Bowei Ma, Yucheng Guo, Wei-An Hu, et al.
Frontiers in Pharmacology (2020) Vol. 11
Open Access | Times Cited: 21

Showing 21 citing articles:

Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis
Ruixin Yang, Yingyan Yu
Frontiers in Oncology (2021) Vol. 11
Open Access | Times Cited: 226
Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy
Clare McGenity, Emily L. Clarke, Charlotte Jennings, et al.
npj Digital Medicine (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 32
Prediction of lymph node metastasis in early colorectal cancer based on histologic images by artificial intelligence
Manabu Takamatsu, Noriko Yamamoto, Hiroshi Kawachi, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 35
Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis
Minhyeok Lee
Bioengineering (2023) Vol. 10, Iss. 8, pp. 897-897
Open Access | Times Cited: 17
Artificial intelligence in gastroenterology and hepatology: Status and challenges
Jiasheng Cao, Ziyi Lu, Mingyu Chen, et al.
World Journal of Gastroenterology (2021) Vol. 27, Iss. 16, pp. 1664-1690
Open Access | Times Cited: 28
Artificial intelligence: Emerging player in the diagnosis and treatment of digestive disease
Haiyang Chen, Peng Ge, Jiayue Liu, et al.
World Journal of Gastroenterology (2022) Vol. 28, Iss. 20, pp. 2152-2162
Open Access | Times Cited: 21
Hybrid Deep Learning Model for Endoscopic Lesion Detection and Classification Using Endoscopy Videos
M Shahbaz Ayyaz, M. Ikram Ullah Lali, Mubbashar Hussain, et al.
Diagnostics (2021) Vol. 12, Iss. 1, pp. 43-43
Open Access | Times Cited: 25
Use of artificial intelligence for the prediction of lymph node metastases in early-stage colorectal cancer: systematic review
Nasya Thompson, Arthur Morley-Bunker, Jared Mclauchlan, et al.
BJS Open (2024) Vol. 8, Iss. 2
Open Access | Times Cited: 3
Artificial Intelligence-based Tumor Segmentation in Mouse Models of Lung Adenocarcinoma
Alena Arlova, Chengcheng Jin, Abigail Wong-Rolle, et al.
Journal of Pathology Informatics (2022) Vol. 13, pp. 100007-100007
Open Access | Times Cited: 13
Clinical application of machine learning‐based pathomics signature of gastric atrophy
Yadi Lan, Bing Han, Tianyu Zhai, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 2
A systematic review on application of deep learning in digestive system image processing
Huangming Zhuang, Jixiang Zhang, Fei Liao
The Visual Computer (2021) Vol. 39, Iss. 6, pp. 2207-2222
Open Access | Times Cited: 15
Survey of Recent Deep Neural Networks with Strong Annotated Supervision in Histopathology
Dominika Petríková, Ivan Cimrák
Computation (2023) Vol. 11, Iss. 4, pp. 81-81
Open Access | Times Cited: 5
Utilizing nanotechnology and advanced machine learning for early detection of gastric cancer surgery
Dan Wu, Jianhua Lu, Nan Zheng, et al.
Environmental Research (2023) Vol. 245, pp. 117784-117784
Closed Access | Times Cited: 5
Improving diagnosis and outcome prediction of gastric cancer via multimodal learning using whole slide pathological images and gene expression
Yuzhang Xie, Qingqing Sang, Qian Da, et al.
Artificial Intelligence in Medicine (2024) Vol. 152, pp. 102871-102871
Closed Access | Times Cited: 1
Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review
Daria Claudia Turtoi, Vlad Dumitru Brata, Victor Incze, et al.
Journal of Clinical Medicine (2024) Vol. 13, Iss. 16, pp. 4818-4818
Open Access | Times Cited: 1
Application of artificial neural network algorithm in pathological diagnosis and prognosis prediction of digestive tract malignant tumors
Ya Xiao, Shuyang Wang, Ling Ren, et al.
Journal of Zhejiang University (Medical Sciences) (2023) Vol. 52, Iss. 2, pp. 243-248
Open Access | Times Cited: 3
Tracking cancer lesions on surgical samples of gastric cancer by artificial intelligent algorithms
Ruixin Yang, Chao Yan, Sheng Lu, et al.
Journal of Cancer (2021) Vol. 12, Iss. 21, pp. 6473-6483
Open Access | Times Cited: 7
Unsupervised Segmentation in NSCLC: How to Map the Output of Unsupervised Segmentation to Meaningful Histological Labels by Linear Combination?
Cleo‐Aron Weis, Kian R. Weihrauch, Katharina Kriegsmann, et al.
Applied Sciences (2022) Vol. 12, Iss. 8, pp. 3718-3718
Open Access | Times Cited: 4
Classification of Ovarian Cancer Subtype Using Histopathology Images
R. Parvathi, Xiaohui Yuan
Advances in information security, privacy, and ethics book series (2024), pp. 59-86
Closed Access
Optimization of Fault Learning in Medical Devices
V. Kakulapati
International Journal of Intelligent Systems and Applications (2022) Vol. 14, Iss. 6, pp. 38-49
Open Access | Times Cited: 1
Artificial intelligence in diagnostic of chronic gastritis, Helicobacter pylori infection and functional dyspepsia
Yu. P. Uspenskiy, Н. В. Барышникова, A. A. Ershova
Medical alphabet (2022), Iss. 12, pp. 27-33
Closed Access
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