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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.

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Showing 26-50 of 47 citing articles:

AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer
Noorul Wahab, Michael S. Toss, Islam M. Miligy, et al.
npj Precision Oncology (2023) Vol. 7, Iss. 1
Open Access | Times Cited: 7
Advancements in triple-negative breast cancer sub-typing, diagnosis and treatment with assistance of artificial intelligence : a focused review
Zahra Batool, Mohammad Amjad Kamal, Bairong Shen
Journal of Cancer Research and Clinical Oncology (2024) Vol. 150, Iss. 8
Open Access | Times Cited: 2
Challenges of artificial intelligence in precision oncology: public-private partnerships including national health agencies as an asset to make it happen
Vinh-Phuc Luu, Matilde Fiorini, Sarah Combes, et al.
Annals of Oncology (2023) Vol. 35, Iss. 2, pp. 154-158
Open Access | Times Cited: 5
PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning
Witali Aswolinskiy, Enrico Munari, Hugo M. Horlings, et al.
Breast Cancer Research (2023) Vol. 25, Iss. 1
Open Access | Times Cited: 5
GSDME-mediated pyroptosis promotes anti-tumor immunity of neoadjuvant chemotherapy in breast cancer
Changfang Fu, Wenbo Ji, Qianwen Cui, et al.
Cancer Immunology Immunotherapy (2024) Vol. 73, Iss. 9
Open Access | Times Cited: 1
Spatial transcriptomics reveals substantial heterogeneity in triple negative breast cancer with potential clinical implications
Xiaoxiao Wang, David Venet, Frédéric Lifrange, et al.
Research Square (Research Square) (2024)
Open Access | Times Cited: 1
Architecture of head and neck squamous cell carcinoma tumor microenvironment revealed: can tertiary lymphoid structures predict post-surgery recurrence?
Jennifer Sosa, Alexandra Glathar, Satrajit Sinha
Translational Cancer Research (2024) Vol. 13, Iss. 2, pp. 484-489
Open Access | Times Cited: 1
PhiHER2: phenotype-informed weakly supervised model for HER2 status prediction from pathological images
Chaoyang Yan, Jialiang Sun, Yiming Guan, et al.
Bioinformatics (2024) Vol. 40, Iss. Supplement_1, pp. i79-i90
Open Access | Times Cited: 1
A glimpse into the future: Integrating artificial intelligence for precision HER2‐positive breast cancer management
Xinpei Deng, Yixuan Yan, Zekai Zhan, et al.
iMetaOmics. (2024) Vol. 1, Iss. 1
Open Access | Times Cited: 1
Histopathology in focus: a review on explainable multi-modal approaches for breast cancer diagnosis
Faseela Abdullakutty, Younes Akbari, Somaya Al-Máadeed, et al.
Frontiers in Medicine (2024) Vol. 11
Open Access | Times Cited: 1
Path2Space: An AI Approach for Cancer Biomarker Discovery Via Histopathology Inferred Spatial Transcriptomics
Eldad D. Shulman, Emma M. Campagnolo, Roshan Lodha, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
CO-PILOT: Dynamic Top-Down Point Cloud with Conditional Neighborhood Aggregation for Multi-Gigapixel Histopathology Image Representation
Ramin Nakhli, Allen Zhang, Ali Khajegili Mirabadi, et al.
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2023), pp. 21006-21016
Closed Access | Times Cited: 3
Spatial transcriptomics reveals substantial heterogeneity in triple-negative breast cancer with potential clinical implications
Xiaoxiao Wang, David Venet, Frédéric Lifrange, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access
Quantification of Decellularization in Hematoxylin and Eosin Stained Images of Decellularized Aorta Using Machine Learning
Naoko NAKAMURA, E Yasuda, Shota AKIYAMA, et al.
Advanced Biomedical Engineering (2024) Vol. 13, pp. 26-34
Open Access
Development of a multiparametric model for predicting the response to neoadjuvant chemotherapy in breast cancer
Fusheng Qian, Yajing Mao, Jie Dong, et al.
Translational Cancer Research (2024) Vol. 13, Iss. 2, pp. 558-568
Open Access
Accessible Machine Learning and Deep Learning Models Predict Response and Survival in Early Stage Hormone Receptor-Positive/HER2-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy
Giovanna Garufi, Luca Mastrantoni, Giulia Giordano, et al.
Research Square (Research Square) (2024)
Open Access
Application of AI in detection of breast cancer with laboratory results monitoring
Sabina Prevljak, Amar Kustura, Berina Hasanefendić, et al.
Bioengineering Studies (2024) Vol. 5, Iss. 1, pp. 1-14
Open Access
Expanding role and scope of artificial intelligence in the field of gastrointestinal pathology
Muhammed Mubarak, Rahma Rashid, Fnu Sapna, et al.
Artificial Intelligence in Gastroenterology (2024) Vol. 5, Iss. 2
Open Access
M2Fusion: Multi-time Multimodal Fusion for Prediction of Pathological Complete Response in Breast Cancer
Song Zhang, Siyao Du, Caixia Sun, et al.
Lecture notes in computer science (2024), pp. 458-468
Closed Access
An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer
Yuan Gao, Sofía Ventura‐Díaz, Xin Wang, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
Philippe Weitz, Masi Valkonen, Leslie Solorzano, et al.
arXiv (Cornell University) (2023)
Open Access
Flexible and Highly-Efficient Feature Perception for Molecular Traits Prediction via Self-interactive Deep Learning
Yang Hu, Korsuk Sirinukunwattana, Bin Li, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access
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