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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:

Multi-scale representation attention based deep multiple instance learning for gigapixel whole slide image analysis
Hangchen Xiang, Junyi Shen, Qingguo Yan, et al.
Medical Image Analysis (2023) Vol. 89, pp. 102890-102890
Closed Access | Times Cited: 23

Showing 23 citing articles:

Interpretable medical deep framework by logits-constraint attention guiding graph-based multi-scale fusion for Alzheimer’s disease analysis
Jinghao Xu, Chenxi Yuan, Xiaochuan Ma, et al.
Pattern Recognition (2024) Vol. 152, pp. 110450-110450
Closed Access | Times Cited: 8
Dual-path neural network extracts tumor microenvironment information from whole slide images to predict molecular typing and prognosis of Glioma
Z. Ning, Bojie Yang, Yuanyuan Wang, et al.
Computer Methods and Programs in Biomedicine (2025) Vol. 261, pp. 108580-108580
Closed Access
Dynamic graph based weakly supervised deep hashing for whole slide image classification and retrieval
Hu Jin, Junyi Shen, Lei Cui, et al.
Medical Image Analysis (2025), pp. 103468-103468
Closed Access
GAMMIL: A graph attention-guided multi-scale fusion multiple instance learning model for the WHO grading of meningioma in whole slide images
Guilan Tu, Wuchao Li, Yan‐Hui Lin, et al.
Biomedical Signal Processing and Control (2025) Vol. 105, pp. 107652-107652
Closed Access
Classification of endoscopic capsule pathologies using Multiple Instance Learning methods
Vitor Bonamigo Moreira, Eduardo Jabbur Machado, Dalila Barbosa, et al.
Procedia Computer Science (2025) Vol. 256, pp. 1116-1124
Open Access
Interpretable multi-stage attention network to predict cancer subtype, microsatellite instability, TP53 mutation and TMB of endometrial and colorectal cancer
Ching‐Wei Wang, Hikam Muzakky, Yu‐Ching Lee, et al.
Computerized Medical Imaging and Graphics (2025), pp. 102499-102499
Closed Access
SD-MIL: Multiple instance learning with dual perception of scale and distance information fusion for whole slide image classification
Yining Xie, Zequn Liu, Jiajun Chen, et al.
Expert Systems with Applications (2025), pp. 126831-126831
Closed Access
Weakly supervised nuclei segmentation based on pseudo label correction and uncertainty denoising
Xipeng Pan, Shilong Song, Zhenbing Liu, et al.
Artificial Intelligence in Medicine (2025), pp. 103113-103113
Closed Access
One label is all you need: Interpretable AI-enhanced histopathology for oncology
Thomas E. Tavolara, Ziyu Su, Metin N. Gürcan, et al.
Seminars in Cancer Biology (2023) Vol. 97, pp. 70-85
Closed Access | Times Cited: 10
Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of endometrial and colorectal cancer
Ching‐Wei Wang, Tzu-Chien Liu, Po-Jen Lai, et al.
Medical Image Analysis (2024) Vol. 99, pp. 103372-103372
Closed Access | Times Cited: 3
Cross-attention guided loss-based deep dual-branch fusion network for liver tumor classification
Rui Wang, Xiaoshuang Shi, Shuting Pang, et al.
Information Fusion (2024), pp. 102713-102713
Closed Access | Times Cited: 2
E2-MIL: An explainable and evidential multiple instance learning framework for whole slide image classification
Jiangbo Shi, Chen Li, Tieliang Gong, et al.
Medical Image Analysis (2024) Vol. 97, pp. 103294-103294
Closed Access | Times Cited: 1
MFMF: Multiple Foundation Model Fusion Networks for Whole Slide Image Classification
Thao Dang, Yuzhi Guo, Hehuan Ma, et al.
(2024), pp. 1-8
Closed Access | Times Cited: 1
Deep learning for endometrial cancer subtyping and predicting tumor mutational burden from histopathological slides
Ching‐Wei Wang, Nabila Puspita Firdi, Yu‐Ching Lee, et al.
npj Precision Oncology (2024) Vol. 8, Iss. 1
Open Access | Times Cited: 1
A novel automatic annotation method for whole slide pathological images combined clustering and edge detection technique
Weilong Ding, Wan‐yin Liao, Xiaojie Zhu, et al.
IET Image Processing (2024) Vol. 18, Iss. 6, pp. 1516-1529
Open Access
Securing Privacy in the Metaverse
Parveen Sharma, Bharath Ramesh, Farrukh Arslan, et al.
Advances in healthcare information systems and administration book series (2024), pp. 58-76
Closed Access
Multiple Instance Pathology Image Diagnosis Model based on Channel Attention and Data Augmentation
Tianjiang Wan, Jingmin Tian, Ping Wei, et al.
Deleted Journal (2024) Vol. 6, Iss. 9
Open Access
Retrosynthetic analysis via deep learning to improve pilomatricoma diagnoses
Zheng Wang, Xinyu Tan, Xue Yang, et al.
Computers in Biology and Medicine (2024) Vol. 182, pp. 109152-109152
Open Access
Knowledge Distillation Based Dual-Branch Network for Whole Slide Image Analysis
Weiheng Fu, M. Xu, Jie Wu, et al.
Lecture notes in computer science (2024), pp. 392-401
Closed Access
A Comparative Study of Pan-Cancer and Tissue-Specific Models for Cancer Detection and Interpretability in Weakly Labeled Whole Slide Images
Daniel Ørnsted Gøtke, Kristoffer Skjødt Thomsen, Jakob Tolbod Schüsler, et al.
(2024)
Closed Access
PHIM-MIL: Multiple instance learning with prototype similarity-guided feature fusion and hard instance mining for whole slide image classification
Yining Xie, Zequn Liu, Jing Zhao, et al.
Information Fusion (2024) Vol. 117, pp. 102847-102847
Closed Access
Machine learning methods for histopathological image analysis: Updates in 2024
Daisuke Komura, Mieko Ochi, Shumpei Ishikawa
Computational and Structural Biotechnology Journal (2024) Vol. 27, pp. 383-400
Open Access
Patch Target Guided Dual-Branch Deep Multiple Instance Learning for 3D MRI Analysis
Ming‐Zhe Dai, Xiaoshuang Shi, Xiaofeng Zhu, et al.
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2024), pp. 903-910
Closed Access
MH-CLR: A Self-Supervised Learning Strategy Based on Hard Sample Mining and Masked Autoencoders
Chenghan Yang, Chao Li, Lei Guo
(2024), pp. 17-23
Closed Access
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