Evaluating the Visualization of What a Deep Neural Network Has Learned

ImageNet classification with deep convolutional neural networks

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Going deeper with convolutions

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ImageNet Large Scale Visual Recognition Challenge

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Learning representations by back-propagating errors

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Learning Multiple Layers of Features from Tiny Images

Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889

Visualizing and understanding convolutional networks

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Explaining and Harnessing Adversarial Examples

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An introduction to variable and feature selection

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Domain-Adversarial Training of Neural Networks

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Intriguing properties of neural networks

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SynthraXCoreNet: An Interpretable, Well-Calibrated Six-CNN Ensemble for Dermoscopic Skin-Lesion Classification

Khan, M. A. A., Tahir, M., Irtaza, S. A., Mughal, M. R., & Yousaf, M. N. (2025) · IEEE Access · doi:10.1109/access.2025.3641868 · Cited by 6

Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations

Rahman, M. M., Calhoun, V. D., & Plis, S. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1129 · Cited by 16

Decoding the multimodal maze: A systematic review on the adoption of explainability in multimodal attention-based models

Kibria, M. R., Lafond, S., & Arslan, J. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104405 · Cited by 12

Layer-Wise Relevance Propagation with Conservation Property for ResNet

Otsuki, S., Iida, T., Doublet, F., Hirakawa, T., Yamashita, T., Fujiyoshi, H., & Sugiura, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72775-7_20 · Cited by 8

Evaluating gradient-based explanation methods for neural network ECG analysis using heatmaps

Storås, A. M., Maeland, S., Isaksen, J. L., Hicks, S. A., Thambawita, V. L., Graff, C., Hammer, H. L., Halvorsen, P., Riegler, M. A., & Kanters, J. K. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae280 · Cited by 5

Application of Explainable Artificial Intelligence ( XAI ) Techniques in Patients With Intracranial Hemorrhage: A Systematic Review

Kohan, A., Zahedi, A., Alizadehsani, R., Tan, R., & Acharya, U. R. (2025) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70031 · Cited by 9

Variable Instance-Level Explainability for Text Classification.

Chrysostomou, G., & Αλέτρας, Ν. (2021) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 5

GrammarSHAP: An Efficient Model-Agnostic and Structure-Aware NLP Explainer

Mosca, E., Demirtürk, D., Mülln, L., Raffagnato, F., & Groh, G. (2022) · doi:10.18653/v1/2022.lnls-1.2 · Cited by 5

Providing Interpretability of Document Classification by Deep Neural Network with Self-attention

Tamekuri, A., Nakamura, K., Takahashi, Y., & Yamaguchi, S. (2022) · Journal of Information Processing · doi:10.2197/ipsjjip.30.397 · Cited by 5

Deep learning and multi-level featurization of graph representations of microstructural data

Jones, R. E., Safta, C., & Frankel, A. (2023) · Computational Mechanics · doi:10.1007/s00466-023-02300-3 · Cited by 5

Explainable AI for Medical Event Prediction for Heart Failure Patients

Wrazen, W., Gontarska, K., Grzelka, F., & Polze, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34344-5_12 · Cited by 5

Optimizing Explanations by Network Canonization and Hyperparameter Search

Pahde, F., Yolcu, G. Ü., Binder, A., Samek, W., & Lapuschkin, S. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00396 · Cited by 5

When an Explanation is not Enough: An Overview of Evaluation Metrics of Explainable AI Systems in the Healthcare Domain

Pietilä, E., & Moreno-Sánchez, P. A. (2024) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-49062-0_60 · Cited by 5

Can We Trust Deep Learning Models in SAR ATR?

Gao, Y., Guo, W., Li, D., & Yu, W. (2024) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2024.3356727 · Cited by 5

Transformers: Statistical interpretation, architectures and applications

Meng, F., & Wang, Y. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/43/20230832 · Cited by 5

Explainability is not Enough : Requirements for Human-AI-Partnership in Complex Socio-Technical Systems

Waefler, T., & Schmid, U. (2020) · Otto-Friedrich-Universität eBooks · doi:10.34190/eair.20.007 · Cited by 5

Comparative Performances of Neural Networks of Variant Architectures Trained with Backpropagation and Differential Evolution

Oussalem, Z., Venkat, R. A., Malagavalli, J., & Bhattacharya, A. K. (2021) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec45853.2021.9504984 · Cited by 5

X3SEG: Model-Agnostic Explanations For The Semantic Segmentation Of 3D Point Clouds With Prototypes And Criticism

Heide, N. F., Muller, E., Petereit, J., & Heizmann, M. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506624 · Cited by 5

Understanding and explaining convolutional neural networks based on inverse approach

Kwon, H. J., Koo, H. I., & Cho, N. I. (2022) · Cognitive Systems Research · doi:10.1016/j.cogsys.2022.10.009 · Cited by 6

Tell Model Where to Attend: Improving Interpretability of Aspect-Based Sentiment Classification via Small Explanation Annotations

Cheng, Z., Zhou, J., Wu, W., Chen, Q., & He, L. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096952 · Cited by 5

Forward Composition Propagation for Explainable Neural Reasoning

Grau, I., Nápoles, G., Bello, M., Salgueiro, Y., & Jastrzębska, A. (2024) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2023.3327834 · Cited by 5

Explaining Data-Driven Control in Autonomous Systems: A Reinforcement Learning Case Study

Zou, M., Perrusquía, A., & Guo, W. (2024) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit62066.2024.10708274 · Cited by 5

A fast search algorithm based on image pyramid for robotic grasping

Ren, G., Shao, Z., Guan, Y., Qu, Y., Tan, J., Wei, H., & Tong, G. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206561 · Cited by 5

Rolling in the Deep Convolutional Neural Networks

Soydemir, D. (2019) · International Journal of Intelligent Systems and Applications in Engineering · doi:10.18201/ijisae.2019457674 · Cited by 5

Deep learning in bioinformatics: introduction, application, and perspective in big data eraPreprint

Li, Y., Huang, C., Ding, L., Li, Z., Pan, Y., & Gao, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.00342 · Cited by 5

Interpretable Deep Learning: Interpretations, Interpretability, Trustworthiness, and Beyond.Preprint

Li, X., Xiong, H., Li, X., Wu, X., Zhang, X., Liu, J., Bian, J., & Dou, D. (2021) · arXiv (Cornell University) · Cited by 5

Beyond Single Path Integrated Gradients for Reliable Input Attribution via Randomized Path Sampling

Jeon, G., Jeong, H., & Choi, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00196 · Cited by 4

Understanding User Preferences in Explainable Artificial Intelligence: A Mapping Function Proposal

Hashemi, M., Darejeh, A., & Cruz, F. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3733837 · Cited by 11

Class-Dependent Perturbation Effects in Evaluating Time Series Attributions

Baer, G. T., Grau, I., Zhang, C., & Gorp, P. M. E. V. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-08330-2_14 · Cited by 4

Are explainable AI (XAI) evaluation strategies aligned? Comparing subjective, objective, and mathematical evaluation measures using saliency maps

Kares, F., Speith, T., Zhang, H., & Langer, M. (2026) · Computers in Human Behavior · doi:10.1016/j.chb.2026.109163 · Cited by 18

The interpretability of the activity signal detection model for wood‐boring pests Semanotus bifasciatus in the larval stage

Liu, X., Chen, Z., Zhang, H., Li, J., Jiang, Q., Ren, L., & Luo, Y. (2023) · Pest Management Science · doi:10.1002/ps.7566 · Cited by 4

Better Understanding Differences in Attribution Methods via Systematic Evaluations

Rao, S., Böhle, M., & Schiele, B. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3353528 · Cited by 4

Inflect-text: a novel mechanism to evade neural text classifiers by leveraging word inflectional perturbations

Bajaj, A., & Vishwakarma, D. K. (2025) · International Journal of Information Security · doi:10.1007/s10207-025-00986-5 · Cited by 4

Explainable AI for raising confidence in deep learning‐based tumor tracking models

Grama, D. I., Dahele, M. R., Slotman, B. J., & Verbakel, W. F. A. R. (2025) · Medical Physics · doi:10.1002/mp.17940 · Cited by 5

Fine-Tuning and Explaining FinBERT for Sector-Specific Financial News: A Reproducible Workflow

Cristescu, M. P., Brândaș, C., Mara, D. A., & Petrea, I. (2025) · Electronics · doi:10.3390/electronics14234680 · Cited by 8

Benchmarking interpretability of deep learning for predictive genomics: Recall, precision, and variability of feature attribution

Reynolds, J., & Pan, C. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013784 · Cited by 4

Enhancing interpretability in image-based ECG exams for atrial fibrillation classification using deep learning

Ribeiro, E., Dias, F. M., Soares, Q. B., Krieger, J. E., & Gutierrez, M. A. (2025) · Research on Biomedical Engineering · doi:10.1007/s42600-025-00432-4 · Cited by 4

KneeXNet-2.5D: a clinically-oriented and explainable deep learning framework for MRI-based knee cartilage and meniscus segmentation

Sanogo, M., Gao, F., Littlefield, N. G., Siddiqui, I. A., Carlson, L., Rezaei, A., Bodanki, P., Menezes, Z., Kanda, A., Abedian, M., et al. (2026) · npj Health Systems · doi:10.1038/s44401-026-00072-5 · Cited by 4

Reliability Evaluation of Visualization Performance of Convolutional Neural Network Models for Automated Driving

Zhang, C., Okafuji, Y., & Wada, T. (2021) · International Journal of Automotive Engineering · doi:10.20485/jsaeijae.12.2_41 · Cited by 4

Measurably Stronger Explanation Reliability Via Model Canonization

Motzkus, F., Weber, L., & Lapuschkin, S. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897282 · Cited by 4

Layer-Wise Relevance Analysis for Motif Recognition in the Activation Pathway of the β2-Adrenergic GPCR Receptor

Gutiérrez-Mondragón, M. A., König, C., & Vellido, A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24021155 · Cited by 4

A Novel Metric for XAI Evaluation Incorporating Pixel Analysis and Distance Measurement

Stodt, J., Reich, C., & Clarke, N. (2023) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai59109.2023.00009 · Cited by 4

6GIoDT: 6G-assisted intelligent resource utilization framework for the Internet of Drone Things

Mukherjee, A., Biswas, S., Dey, N., & De, D. (2024) · Wireless Networks · doi:10.1007/s11276-024-03771-9 · Cited by 4

Toward Understanding the Disagreement Problem in Neural Network Feature Attribution

Koenen, N., & Wright, M. N. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63800-8_13 · Cited by 3

Epileptic signal classification using convolutional neural network and Shapley additive explainable artificial intelligence method

Rathod, P., Naik, S. T., & Bhalodiya, J. M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10915-7 · Cited by 4

SDN-DDoSNet: A Deep Learning Framework for DDoS Attack Detection in Software-Defined Networks

Ojha, A., Yadav, A., & Singh, V. (2024) · IEEE Conference on Information and Communication Technology (CICT) · doi:10.1109/cict64037.2024.10899627 · Cited by 4

From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation

Wickstrøm, K., Höhne, M., & Hedström, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92648-8_15 · Cited by 8

Towards bandwidth efficient edge–cloud collaborative deep learning with Data Importance driven Compression

Jiang, Y., Zhao, P. F., Zhao, C., & Lin, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130835 · Cited by 4

A General Approach to Compute the Relevance of Middle-Level Input Features

Apicella, A., Giugliano, S., Isgrò, F., & Prevete, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68796-0_14 · Cited by 4

An Interactive Graphical Visualization Approach to CNNs and RNNs

Vyas, A., & Calyam, P. (2020) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr50011.2020.9425299 · Cited by 4

Attending via both Fine-tuning and Compressing

Zhou, J., Wu, Y., Chen, Q., Huang, X., & He, L. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.189 · Cited by 4

Introduction to Deep Learning in Clinical Neuroscience

de Dios, E., Ali, M. B., Gu, I. Y., Vecchio, T. G., Ge, C., & Jakola, A. S. (2021) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-030-85292-4_11 · Cited by 4

Decrypting the Black Boxing of Artificial Intelligence Using Explainable Artificial Intelligence in Smart Healthcare

Shaikh, T. A., Mir, W. A., & Sofi, S. A. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-97929-4_3 · Cited by 5

A Heatmap-Based Approach for Analyzing Traffic Sign Recognition and Lane Detection Algorithms

Nieroda, B., Wojakowski, T., Skruch, P., & Szelest, M. (2022) · International Conference on Methods and Models in Automation and Robotics · doi:10.1109/mmar55195.2022.9874299 · Cited by 4

Providing Post-Hoc Explanation for Node Representation Learning Models Through Inductive Conformal Predictions

Park, H. (2022) · IEEE Access · doi:10.1109/access.2022.3233036 · Cited by 5

Transformer in mRNA Degradation Prediction

Yit, T. W., hassan, R. B., Zakaria, N. H., Kasim, S. B., Moi, S. H., Khairuddin, A. R., & Amnur, H. (2023) · JOIV International Journal on Informatics Visualization · doi:10.30630/joiv.7.2.1165 · Cited by 4

Similarizing the Influence of Words with Contrastive Learning to Defend Word-level Adversarial Text Attack

Zhan, P., Jing, Y., Wang, H., Zheng, C., Huang, X. B., & Wang, L. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.500 · Cited by 4

Analyzing and Diagnosing Pose Estimation with Attributions

He, Q., Yang, L., Gu, K., Lin, Q., & Yao, A. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00467 · Cited by 4

Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary Algorithm

Zhang, J., Zhang, P., & Xu, B. (2019) · Complexity · doi:10.1155/2019/1712569 · Cited by 4

Understanding the Behavior of Data-Driven Inertial Odometry With Kinematics-Mimicking Deep Neural Network

Dugne-Hennequin, Q. A., Uchiyama, H., & Lima, J. P. (2021) · IEEE Access · doi:10.1109/access.2021.3062817 · Cited by 4

A psychophysics approach for quantitative comparison of interpretable computer vision modelsPreprint

Bießmann, F., & Refiano, D. I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.05011 · Cited by 5

Different Perspective of Machine Learning Technique to Better Predict Breast Cancer SurvivalPreprint

Kumar, K., Singh, V. V., & Ramaswamy, R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.03.186890 · Cited by 4

Convolutional Neural Network Interpretability with General Pattern TheoryPreprint

Tjoa, E., & Cuntai, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.04247 · Cited by 4

Early Detection of Type 2 Diabetes Mellitus Using Deep Neural Network–Based Model

Sapra, V., & Sapra, L. (2022) · doi:10.1002/9781119769293.ch15 · Cited by 4

Robust Malware Detection using Residual Attention NetworkPreprint

Ganesan, S., Vinayakumar, R., Krichen, M., Sowmya, V., Alroobaea, R., & Soman, K. P. (2020) · doi:10.36227/techrxiv.13385291 · Cited by 4

Sanity Checks for Saliency Metrics

Tomsett, R., Harborne, D., Chakraborty, S., Gurram, P., & Preece, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6064 · Cited by 3

Graph visualization efficiency of popular web-based libraries

Zhao, X., Wang, X., Zou, X., Liang, H., Bai, G., Zhang, N., Huang, X. Y., Zhou, F., & Zhao, Y. (2025) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-025-00193-y · Cited by 3

Leveraging Sentinel-1/2 time series and deep learning for accurate forest tree species mapping

Tan, J. H., Li, Y., Ma, T., Yan, X., & Huo, Z. (2025) · Frontiers in Forests and Global Change · doi:10.3389/ffgc.2025.1599510 · Cited by 3

BALANCE: Interpretable breast cancer classification via dual-granularity attention with latent prototype alignment

Cai, J., Zhao, Y., Zhang, H., Tong, Y., Wang, J., Zhang, K., Zhou, F., Yin, J., & Jing, J. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.131030 · Cited by 4

Interpretable long-horizon air pollution forecasting using a transformer-based framework

Zhang, Z., Ma, X., Engardt, M., Schlesinger, D., & Johansson, C. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131719 · Cited by 3

Dual-domain knowledge fusion for interpretable knowledge tracing

Zhou, T., Li, G., Yanting, L. I., Zhang, R., & Ju, S. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115715 · Cited by 4

Data-driven approaches for functional ceramics

Qin, J., Zhang, F., Ma, M., Li, Y., & Liu, Z. (2026) · Materials Science and Engineering R Reports · doi:10.1016/j.mser.2026.101213 · Cited by 3

Explainable AI in Medicine: A Comprehensive Narrative Review of Methods, Applications, and Future Directions

Wang, H. J., Shi, G., Liu, X., Zhang, J., Chen, S., Shi, J., Li, Y., Yue, G., & Wu, Y. (2026) · Expert Systems · doi:10.1111/exsy.70259 · Cited by 5

A Novel Approach to Image Colorization Algorithm using Convolutional Neural Networks

Singla, M., Jain, A., Mantri, M., Tulshain, S., Muthu, R. K., & Naidu, R. C. (2022) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic53929.2022.9793234 · Cited by 3

HIEF: a holistic interpretability and explainability framework

Kucklick, J.-P. (2023) · Journal of Decision System · doi:10.1080/12460125.2023.2207268 · Cited by 6

ExplainLFS: Explaining neural architectures for similarity learning from local perturbations in the latent feature space

Bello, M., Costa, P., Nápoles, G., Mesejo, P., & Cordón, Ó. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102407 · Cited by 3

DocXplain: A Novel Model-Agnostic Explainability Method for Document Image Classification

Saifullah, S., Agne, S., Dengel, A. R., & Ahmed, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70546-5_7 · Cited by 5

A Feature Importance Analyzable Resilient Deep Neural Network for Road Safety Performance Function Surrogate Modeling

Pan, G., Liu, C., Wang, G., Wei, H., & Qiao, J. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3485770 · Cited by 3

On the Evaluation Consistency of Attribution-Based Explanations

Duan, J., Li, H., Zhang, H., Jiang, H., Xue, M., Li, S., Song, M., & Song, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72897-6_12 · Cited by 7

How informative is your XAI? Assessing the quality of explanations through information power

Matarese, M., Rea, F., Rohlfing, K. J., & Sciutti, A. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2024.1412341 · Cited by 3

An Experimental Investigation into the Evaluation of Explainability Methods for Computer Vision

Stassin, S., Englebert, A., Nanfack, G., Albert, J., Versbraegen, N., Peiffer, G., Doh, M., Riche, N., Frénay‬, B., & Vleeschouwer, C. D. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-74630-7_7 · Cited by 10

Visagreement: Visualizing and Exploring Explanations (Dis)Agreement

Silva, P., Guardieiro, V., Barr, B., Silva, C., & Nonato, L. G. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3558074 · Cited by 5

Real time image processing and smart healthcare using eXplainable artificial intelligence (XAI)

Aggarwal, L., Ranjan, V., & Sharma, A. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105653 · Cited by 3

Don’t get me wrong: How to apply deep visual interpretations to time series

Löffler, C., Lai, W.-C., Zanca, D., Schmidt, L., Eskofier, B. M., & Mutschler, C. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06798-3 · Cited by 12

Leveraging Gaussian Process Approximations for Rapid Image Overlay Production

Burke, M. (2017) · doi:10.1145/3132711.3132715 · Cited by 3

Temporal Task Scheduling for Delay-Constrained Applications in Geo-Distributed Cloud Data Centers

Bi, J., Yuan, H., Zhang, J., & Zhou, M. (2018) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2018.00025 · Cited by 3

Evaluating Layer-Wise Relevance Propagation Explainability Maps for Artificial Neural Networks

Ranguelova, E., Pauwels, E., & Berkhout, J. (2018) · IEEE International Conference on eScience · doi:10.1109/escience.2018.00107 · Cited by 3

Distinguishing between Dementia with Lewy bodies (DLB) and Alzheimer’s Disease (AD) using Mental Health Records: a Classification Approach

Wang, Z., Ive, J., Moylett, S., Mueller, C., Cardinal, R. N., Velupillai, S. U., O'Brien, J. T., & Stewart, R. J. (2020) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2020.clinicalnlp-1.19 · Cited by 3

Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks

Carbone, G., Bortolussi, L., & Sanguinetti, G. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892788 · Cited by 5

What Boosts Fake News Dissemination on Social Media? A Causal Inference View

Li, Y., Lee, K., Kordzadeh, N., & Guo, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33383-5_19 · Cited by 3

SIGNed explanations: Unveiling relevant features by reducing bias

Gumpfer, N., Prim, J., Keller, T., Seeger, B., Guckert, M., & Hannig, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101883 · Cited by 6

Development of Trustable Deep Learning Model in Remote Sensing through Explainable-AI Method Selection

Tasneem, S., & Islam, K. A. (2023) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon59035.2023.10316012 · Cited by 4

SNIPPET: A Framework for Subjective Evaluation of Visual Explanations Applied to DeepFake Detection

Yang, Y., Joukovsky, B., Jose Oramas, M., Tuytelaars, T., & Deligiannis, N. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3665248 · Cited by 4

A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors

Gutiérrez-Mondragón, M. A., Vellido, A., & König, C. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25126572 · Cited by 3

An Accurate Deep Neural Network-Based Inversion Approach for Multichannel Microwave Interferometer Characterization of Plasma Wake Electron Density Distribution

Zhou, Z., Tian, J., Ma, P., Tang, P., Lei, S., Chen, B., & Tian, D. (2024) · IEEE Antennas and Wireless Propagation Letters · doi:10.1109/lawp.2024.3430557 · Cited by 3

Improving Network Interpretability via Explanation Consistency Evaluation

Wu, H., Jiang, H., Wang, K., Tang, Z., He, X., & Lin, L. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3453058 · Cited by 3

Supporting User Critiques of AI Systems via Training Dataset Explanations: Investigating Critique Properties and the Impact of Presentation Style

Anik, A. I., & Bunt, A. (2024) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl/hcc60511.2024.00024 · Cited by 4

A human-on-the-loop approach for labelling seismic recordings from landslide site via a multi-class deep-learning based classification model

Jiang, J., Murray, D., Stanković, V., Stanković, L., Hibert, C., Pytharouli, S. I., & Malet, J. (2025) · Science of Remote Sensing · doi:10.1016/j.srs.2024.100189 · Cited by 3

A Novel Visual Interpretability for Deep Neural Networks by Optimizing Activation Maps with Perturbation

Zhang, Q., Rao, L., & Yang, Y.-B. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i4.16450 · Cited by 27

Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks

Nam, W.-J., Gur, S., Choi, J., Wolf, L., & Lee, S.-W. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5632 · Cited by 98

Visual Interpretation of CNN Prediction Through Layerwise Sequential Selection of Discernible Neurons

Iqbal, M. T. B., Muqeet, A., & Bae, S. (2022) · IEEE Access · doi:10.1109/access.2022.3188394 · Cited by 2

Regional Multi-Scale Approach for Visually Pleasing Explanations of Deep Neural Networks

Seo, D., Oh, K., & Oh, I.-S. (2019) · IEEE Access · doi:10.1109/access.2019.2963055 · Cited by 31

Visualizations of Deep Neural Networks in Computer Vision: A Survey

Seifert, C., Aamir, A., Balagopalan, A., Jain, D., Sharma, A., Grottel, S., & Gumhold, S. (2017) · Studies in big data · doi:10.1007/978-3-319-54024-5_6 · Cited by 75