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Keyword

deep learning interpretability

Description: Methods for explaining or analyzing how deep neural networks produce their predictions, particularly in image analysis and neuroimaging.
OpenAlex ID: keywords/deep-learning-interpretability
Causability and explainability of artificial intelligence in medicine
2019 · Andreas Holzinger, Georg Langs, et al. · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · 1,877
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Drug discovery with explainable artificial intelligence
2020 · José Jiménez-Luna, Francesca Grisoni, et al. · Nature Machine Intelligence · 1,055
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Explainable artificial intelligence: a comprehensive review
2021 · Dang Minh, H. Xiang Wang, et al. · Artificial Intelligence Review · 978
Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
2020 · Christoph Molnar, Giuseppe Casalicchio, et al. · Communications in computer and information science · 674
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Survey of Explainable AI Techniques in Healthcare
2023 · Ahmad Chaddad, Jihao Peng, et al. · Sensors · 588
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Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
2018 · Ahmed Hosny, Chintan Parmar, et al. · PLoS Medicine · 577
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Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications
2021 · Yu-Liang Chou, Catarina Moreira, et al. · Information Fusion · 283
Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches
2023 · Katarzyna Borys, Yasmin Alyssa Schmitt, et al. · European Journal of Radiology · 232
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A survey of visual analytics for Explainable Artificial Intelligence methods
2021 · Gulsum Alicioglu, Bo Sun · Computers & Graphics · 221
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations
2019 · Fred Hohman, Haekyu Park, et al. · IEEE Transactions on Visualization and Computer Graphics · 219
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Interpretation and visualization techniques for deep learning models in medical imaging
2020 · Daniel T Huff, Amy J Weisman, et al. · Physics in Medicine and Biology · 169
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Bridging the Gap Between AI and Explainability in the GDPR: Towards Trustworthiness-by-Design in Automated Decision-Making
2022 · Ronan Hamon, Henrik Junklewitz, et al. · IEEE Computational Intelligence Magazine · 160
Review Study of Interpretation Methods for Future Interpretable Machine Learning
2020 · Jian-Xun Mi, An-Di Li, et al. · IEEE Access · 159
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Explainable Artificial Intelligence Applications in NLP, Biomedical, and Malware Classification: A Literature Review
2019 · Sherin Mary Mathews · Advances in intelligent systems and computing · 153
Interpretability research of deep learning: A literature survey
2024 · Biao Xu, Guanci Yang · Information Fusion · 150
A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging
2024 · Deepshikha Bhati, FNU Neha, et al. · Journal of Imaging · 139
Semantics of the Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable and Explainable?
2021 · Manas Gaur, Keyur Faldu, et al. · IEEE Internet Computing · 139
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Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
2021 · Rojat, Thomas, Puget, Raphaël, et al. · arXiv (Cornell University) · 116
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A Perspective on Explanations of Molecular Prediction Models
2023 · Geemi P. Wellawatte, Heta A. Gandhi, et al. · Journal of Chemical Theory and Computation · 107
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Covid-19 and Artificial Intelligence: Genome sequencing, drug development and vaccine discovery
2022 · Sali Abubaker Bagabir, Nahla Khamis Ibrahim, et al. · Journal of Infection and Public Health · 106
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On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning
2021 · Eoin M. Kenny, Mark T Keane · Proceedings of the AAAI Conference on Artificial Intelligence · 104
Benchmarking Deep Learning Interpretability in Time Series Predictions
2020 · Ismail, Aya Abdelsalam, Gunady, Mohamed, et al. · arXiv (Cornell University) · 104
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Model-Agnostic Interpretability with Shapley Values
2019 · Andreas Messalas, Yiannis Kanellopoulos, et al. · International Conference on Information, Intelligence, Systems and Applications · 104
Interpreting deep learning models for epileptic seizure detection on EEG signals
2021 · Valentin Gabeff, Tomas Teijeiro, et al. · Artificial Intelligence in Medicine · 97
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Ensemble Image Explainable AI (XAI) Algorithm for Severe Community-Acquired Pneumonia and COVID-19 Respiratory Infections
2022 · Lin Zou, Han Leong Goh, et al. · IEEE Transactions on Artificial Intelligence · 93
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Works count: 651
Citation count: 12,380
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