Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Benchmarking saliency methods for chest X-ray interpretation
Adriel Saporta, Xiaotong Gui, Ashwin Agrawal, et al.
Nature Machine Intelligence (2022) Vol. 4, Iss. 10, pp. 867-878
Open Access | Times Cited: 108

Showing 51-75 of 108 citing articles:

Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification
Han Yuan, Chuan Hong, Peng-Tao Jiang, et al.
Journal of Biomedical Informatics (2024) Vol. 156, pp. 104673-104673
Open Access | Times Cited: 2
Artificial Intelligence-Enabled Electrocardiography Predicts Future Pacemaker Implantation and Adverse Cardiovascular Events
Yuan Hung, C.-H. Lin, Chin‐Sheng Lin, et al.
Journal of Medical Systems (2024) Vol. 48, Iss. 1
Closed Access | Times Cited: 2
Multimodal Self-Supervised Learning for Lesion Localization
Hao Yang, Hong-Yu Zhou, Cheng Li, et al.
(2024), pp. 1-5
Open Access | Times Cited: 2
Current status and future directions of explainable artificial intelligence in medical imaging
Shier Nee Saw, Yet Yen Yan, Kwan Hoong Ng
European Journal of Radiology (2024) Vol. 183, pp. 111884-111884
Closed Access | Times Cited: 2
Anatomic boundary‐aware explanation for convolutional neural networks in diagnostic radiology
Han Yuan
iRadiology (2024)
Open Access | Times Cited: 2
Saliency of breast lesions in breast cancer detection using artificial intelligence
Said Pertuz, David Gallego‐Ortega, Érika Suarez, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 5
Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis
Fabi Prezja, Leevi Annala, Sampsa Kiiskinen, et al.
Algorithms (2023) Vol. 17, Iss. 1, pp. 8-8
Open Access | Times Cited: 5
Saliency Map and Deep Learning in Binary Classification of Brain Tumours
Wojciech Chmiel, Joanna Kwiecień, Kacper Motyka
Sensors (2023) Vol. 23, Iss. 9, pp. 4543-4543
Open Access | Times Cited: 4
Designing User-Centric Explanations for Medical Imaging with Informed Machine Learning
Luis Oberste, Florian Rüffer, Okan Aydingül, et al.
Lecture notes in computer science (2023), pp. 470-484
Closed Access | Times Cited: 4
Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis
Andrea M. Storås, Ole Emil Andersen, Sam Lockhart, et al.
Diagnostics (2023) Vol. 13, Iss. 14, pp. 2345-2345
Open Access | Times Cited: 4
Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study
Hongbo Chen, Myrtede Alfred, Andrew D. Brown, et al.
JMIR Formative Research (2024) Vol. 8, pp. e59045-e59045
Open Access | Times Cited: 1
Development and Validation of a Deep Learning Model for Detecting Signs of Tuberculosis on Chest Radiographs among US-bound Immigrants and Refugees
Scott Lee, Shannon Fox, Raheem Smith, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
Generalizability of Deep Neural Networks for Vertical Cup-to-Disc Ratio Estimation in Ultra-Widefield and Smartphone-Based Fundus Images
Boon Peng Yap, Kelvin Z. Li, En Qi Toh, et al.
Translational Vision Science & Technology (2024) Vol. 13, Iss. 4, pp. 6-6
Open Access | Times Cited: 1
SRFAMap: A Method for Mapping Integrated Gradients of a CNN Trained with Statistical Radiomic Features to Medical Image Saliency Maps
Oleksandr Davydko, Vladimir Pavlov, Przemysław Biecek, et al.
Communications in computer and information science (2024), pp. 3-23
Closed Access | Times Cited: 1
Simulating clinical features on chest radiographs for medical image exploration and CNN explainability using a style-based generative adversarial autoencoder
Kyle Hasenstab, Lewis D. Hahn, Nicholas S.Y. Chao, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 1
Explainable AI improves task performance in human–AI collaboration
Julian Senoner, Simon Schallmoser, Bernhard Kratzwald, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 1
The Emerging Role of Artificial Intelligence in Valvular Heart Disease
Caroline Canning, James C. Y. Guo, Akhil Narang, et al.
Heart Failure Clinics (2023) Vol. 19, Iss. 3, pp. 391-405
Open Access | Times Cited: 3
In-Depth Evaluation of Saliency Maps for Interpreting Convolutional Neural Network Decisions in the Diagnosis of Glaucoma Based on Fundus Imaging
José Sigut, Francisco Fumero, J.I. Estévez, et al.
Sensors (2023) Vol. 24, Iss. 1, pp. 239-239
Open Access | Times Cited: 3
Artificial Intelligence Helps to Predict Recurrence and Mortality for Prostate Cancer Using Histology Images
Okyaz Eminağa, Fred Saad, Zhe Tian, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 2
Interpretable surrogate models to approximate the predictions of convolutional neural networks in glaucoma diagnosis
José Sigut, Francisco Fumero, Rafael Arnay, et al.
Machine Learning Science and Technology (2023) Vol. 4, Iss. 4, pp. 045024-045024
Open Access | Times Cited: 2
Artificial Intelligence–Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review
Hazrat Ali, Rizwan Qureshi, Zubair Shah
JMIR Medical Informatics (2023) Vol. 11, pp. e47445-e47445
Open Access | Times Cited: 2
Explainable AI for the Classification of Brain MRIs
Nathan Blake, D.M. Kelly, S. de la Peña, et al.
Research Square (Research Square) (2024)
Open Access
Artificial intelligence-aided data mining of medical records for cancer detection and screening
Amalie Dahl Haue, Jessica Xin Hjaltelin, Peter Christoffer Holm, et al.
The Lancet Oncology (2024) Vol. 25, Iss. 12, pp. e694-e703
Closed Access
How can data augmentation improve attribution maps for disease subtype explainability
Elina Thibeau‐Sutre, Jelmer M. Wolterink, Olivier Colliot, et al.
Medical Imaging 2022: Image Processing (2023), pp. 81-81
Open Access | Times Cited: 1
Learning disentangled representations for explainable chest x-ray classification using Dirichlet VAEs
Rachael Harkness, Alejandro F. Frangi, Kieran Zucker, et al.
Medical Imaging 2022: Image Processing (2023), pp. 32-32
Open Access | Times Cited: 1

Scroll to top