Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works

Microsoft COCO: Common Objects in Context

Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · see citing works

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works

Rethinking the Inception Architecture for Computer Vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works

Learning Deep Features for Discriminative Localization

Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428 · see citing works

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works

“Why Should I Trust You?”: Explaining the Predictions of Any Classifier

Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682 · see citing works

Show and tell: A neural image caption generator

Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298935 · Cited by 6,318 · see citing works

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works

Deep visual-semantic alignments for generating image descriptions

Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902 · see citing works

VQA: Visual Question Answering

Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487 · see citing works

Striving for Simplicity: The All Convolutional Net

Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014) · arXiv · doi:10.48550/arxiv.1412.6806 · Cited by 3,447 · see citing works

Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks

Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150 · see citing works

Microsoft COCO Captions: Data Collection and Evaluation Server

Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollar, P., & Zitnick, C. L. (2015) · arXiv · doi:10.48550/arxiv.1504.00325 · Cited by 1,614 · see citing works

Network Dissection: Quantifying Interpretability of Deep Visual Representations

Bau, D., Zhou, B., Khosla, A., Oliva, A., & Torralba, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.354 · Cited by 1,389 · see citing works

From captions to visual concepts and back

Fang, H., Gupta, S., Iandola, F., Srivastava, R. K., Deng, L., Dollár, P., Gao, J., He, X., Mitchell, M., Platt, J. C., et al. (2015) · 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/cvpr.2015.7298754 · Cited by 1,305 · see citing works

Hierarchical Question-Image Co-Attention for Visual Question AnsweringPreprint

Lu, J., Yang, J., Batra, D., & Parikh, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.00061 · Cited by 1,303 · see citing works

DenseCap: Fully Convolutional Localization Networks for Dense Captioning

Johnson, J., Karpathy, A., & Fei-Fei, L. (2016) · 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/cvpr.2016.494 · Cited by 1,161 · see citing works

Is object localization for free? - Weakly-supervised learning with convolutional neural networks

Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298668 · Cited by 886 · see citing works

Object Detectors Emerge in Deep Scene CNNsPreprint

Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., & Torralba, A. B. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6856 · Cited by 823 · see citing works

Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation

Kolesnikov, A., & Lampert, C. H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_42 · Cited by 773 · see citing works

From image-level to pixel-level labeling with Convolutional Networks

Pinheiro, P. O., & Collobert, R. (2015) · 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/cvpr.2015.7298780 · Cited by 691 · see citing works

Ask Your Neurons: A Neural-Based Approach to Answering Questions about Images

Malinowski, M., Rohrbach, M., & Fritz, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.9 · Cited by 633 · see citing works

Diagnosing Error in Object Detectors

Hoiem, D., Chodpathumwan, Y., & Dai, Q. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33712-3_25 · Cited by 520 · see citing works

Visualizing Deep Convolutional Neural Networks Using Natural Pre-images

Mahendran, A., & Vedaldi, A. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0911-8 · Cited by 487 · see citing works

Visual Dialog

Das, A., Kottur, S., Gupta, K., Singh, A., Yadav, D., Moura, J. M. F., Parikh, D., & Batra, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.121 · Cited by 445 · see citing works

Human Attention in Visual Question Answering: Do Humans and Deep Networks look at the same regions?

Das, A., Agrawal, H., Zitnick, C. L., Parikh, D., & Batra, D. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1092 · Cited by 431 · see citing works

Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning

Cinbis, R. G., Verbeek, J., & Schmid, C. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2535231 · Cited by 420 · see citing works

GuessWhat?! Visual Object Discovery through Multi-modal Dialogue

de Vries, H., Strub, F., Chandar, S., Pietquin, O., Larochelle, H., & Courville, A. C. (2017) · 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/cvpr.2017.475 · Cited by 378 · see citing works

Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning

Das, A., Kottur, S., Moura, J. M. F., Lee, S., & Batra, D. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.321 · Cited by 362 · see citing works

DevNet: A Deep Event Network for multimedia event detection and evidence recounting

Gan, C., Wang, N., Yang, Y., Yeung, D., & Hauptmann, A. G. (2015) · 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/cvpr.2015.7298872 · Cited by 326 · see citing works

Analyzing the Behavior of Visual Question Answering Models

Agrawal, A., Batra, D., & Parikh, D. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1203 · Cited by 297 · see citing works

HOGgles: Visualizing Object Detection Features

Vondrick, C., Khosla, A., Malisiewicz, T., & Torralba, A. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.8 · Cited by 282 · see citing works

Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded

Selvaraju, R. R., Lee, S., Shen, Y., Jin, H., Ghosh, S., Heck, L. P., Batra, D., & Parikh, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00268 · Cited by 253 · see citing works

Top-Down Neural Attention by Excitation Backprop

Zhang, J., Lin, Z., Brandt, J., Shen, X., & Sclaroff, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_33 · Cited by 246 · see citing works

Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based LocalizationPreprint

Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., & Batra, D. (2016) · arXiv (Cornell University) · Cited by 196 · see citing works

Self-taught object localization with deep networks

Bazzani, L., Bergamo, A., Anguelov, D., & Torresani, L. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477688 · Cited by 140 · see citing works

Salient Deconvolutional Networks

Mahendran, A., & Vedaldi, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_8 · Cited by 124 · see citing works

Inverting Convolutional Networks with Convolutional Networks.Preprint

Dosovitskiy, A., & Brox, T. (2015) · arXiv (Cornell University) · Cited by 75 · see citing works

Becoming the expert - interactive multi-class machine teaching

Johns, E., Aodha, O. M., & Brostow, G. (2015) · 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/cvpr.2015.7298877 · Cited by 57 · see citing works

Choose Your Neuron: Incorporating Domain Knowledge Through Neuron-Importance

Selvaraju, R. R., Chattopadhyay, P., Elhoseiny, M., Sharma, T., Batra, D., Parikh, D., & Lee, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_32 · Cited by 43 · see citing works

CloudCV: Large-Scale Distributed Computer Vision as a Cloud Service

Agrawal, H., Mathialagan, C. S., Goyal, Y., Chavali, N., Banik, P., Mohapatra, A., Osman, A., & Batra, D. (2015) · Springer eBooks · doi:10.1007/978-3-319-24702-1_11 · Cited by 38 · see citing works

Definitions, methods, and applications in interpretable machine learning

Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167 · see citing works

Towards Total Recall in Industrial Anomaly Detection

Roth, K., Pemula, L., Zepeda, J., Scholkopf, B., Brox, T., & Gehler, P. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01392 · Cited by 1,810 · see citing works

Attentional Feature Fusion

Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., & Barnard, K. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00360 · Cited by 1,274 · see citing works

Human-computer collaboration for skin cancer recognition

Tschandl, P., Rinner, C., Apalla, Z., Argenziano, G., Codella, N., Halpern, A., Janda, M., Lallas, A., Longo, C., Malvehy, J., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0942-0 · Cited by 1,024 · see citing works

Explainable artificial intelligence: a comprehensive review

Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10088-y · Cited by 983 · see citing works

Addressing bias in big data and AI for health care: A call for open science

Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100347 · Cited by 903 · see citing works

Strong-Weak Distribution Alignment for Adaptive Object Detection

Saito, K., Ushiku, Y., Harada, T., & Saenko, K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00712 · Cited by 819 · see citing works

Multimodal deep learning for biomedical data fusion: a review

Stahlschmidt, S. R., Ulfenborg, B., & Synnergren, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab569 · Cited by 766 · see citing works

Application of explainable artificial intelligence for healthcare: a systematic review of the last decade (2011-2022)

Loh, H. W., Ooi, C. P., Seoni, S., Barua, P. D., Molinari, F., & Acharya, U. R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107161 · Cited by 734 · see citing works

Artificial intelligence for waste management in smart cities: a review

Fang, B., Yu, J., Chen, Z., Osman, A. I., Farghali, M., Ihara, I., Hamza, E. H., Rooney, D. W., & Yap, P.-S. (2023) · Environmental Chemistry Letters · doi:10.1007/s10311-023-01604-3 · Cited by 629 · see citing works

Artificial intelligence for the detection of COVID-19 pneumonia on chest ct using multinational datasets

Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17971-2 · Cited by 587 · see citing works

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

Zheng, X., Yao, Z., Huang, Y., Yu, Y., Wang, Y., Liu, Y., Mao, R., Li, F., Xiao, Y., Wang, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15027-z · Cited by 607 · see citing works

A Review of Explainable Artificial Intelligence in Healthcare

Sadeghi, Z., Alizadehsani, R., CIFCI, M. A., Kausar, S., Rehman, R., Mahanta, P., Bora, P. K., Almasri, A., Alkhawaldeh, R. S., Hussain, S., et al. (2024) · Computers and Electrical Engineering · doi:10.1016/j.compeleceng.2024.109370 · Cited by 598 · see citing works

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

Zhang, Z., Hamadi, H. A., Damiani, E., Yeun, C. Y., & Taher, F. (2022) · IEEE Access · doi:10.1109/access.2022.3204051 · Cited by 450 · see citing works

Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study

Yamashita, R., Long, J., Longacre, T., Peng, L., Berry, G., Martin, B., Higgins, J., Rubin, D. L., & Shen, J. (2021) · The Lancet Oncology · doi:10.1016/s1470-2045(20)30535-0 · Cited by 391 · see citing works

Visual Language Maps for Robot Navigation

Huang, C., Mees, O., Zeng, A., & Burgard, W. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10160969 · Cited by 376 · see citing works

Radimagenet: an open Radiologic deep learning research Dataset for effective transfer learning

Mei, X., Liu, Z., Robson, P. M., Marinelli, B., Huang, M., Doshi, A., Jacobi, A., Cao, C., Link, K. E., Yang, T., et al. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210315 · Cited by 341 · see citing works

Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments

Bai, X., Wang, X., Liu, X., Liu, Q., Song, J., Sebe, N., & Kim, B. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108102 · Cited by 313 · see citing works

Expert-level prenatal detection of complex congenital heart disease from screening ultrasound using deep learning

Arnaout, R., Curran, L., Zhao, Y., Levine, J. C., Chinn, E., & Moon-Grady, A. J. (2020) · doi:10.1101/2020.06.22.20137786 · Cited by 315 · see citing works

Benchmarking saliency methods for chest X-ray interpretation

Saporta, A., Gui, X., Agrawal, A., Pareek, A., Truong, S. Q. H., Nguyen, C. D. T., Ngo, V.-D., Seekins, J., Blankenberg, F. G., Ng, A. Y., et al. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00536-x · Cited by 270 · see citing works

Detection of anaemia from retinal fundus images via deep learning

Mitani, A., Huang, A., Venugopalan, S., Corrado, G. S., Peng, L., Webster, D. R., Hammel, N., Liu, Y., & Varadarajan, A. V. (2019) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0487-z · Cited by 230 · see citing works

A survey of visual analytics for Explainable Artificial Intelligence methods

Alicioglu, G., & Sun, B. (2022) · Computers & Graphics · doi:10.1016/j.cag.2021.09.002 · Cited by 221 · see citing works

Explainability of Deep Vision-Based Autonomous Driving Systems: Review and Challenges

Zablocki, É., Ben-Younes, H., Pérez, P., & Cord, M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01657-x · Cited by 227 · see citing works

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

Wu, M., Hughes, M., Parbhoo, S., Zazzi, M., Roth, V., & Doshi-Velez, F. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11501 · Cited by 213 · see citing works

Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning

Mascharka, D., Tran, P., Soklaski, R., & Majumdar, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00519 · Cited by 203 · see citing works

Explainable artificial intelligence (XAI) for intrusion detection and mitigation in intelligent connected vehicles: a review

Nwakanma, C. I., Ahakonye, L. A. C., Njoku, J. N., Odirichukwu, J. C., Okolie, S. A., Uzondu, C., Ndubuisi Nweke, C. C., & Kim, D.-S. (2023) · Applied Sciences · doi:10.3390/app13031252 · Cited by 202 · see citing works

Explainable artificial intelligence in Alzheimer’s disease classification: a systematic review

Viswan, V., Shaffi, N., Mahmud, M., Subramanian, K., & Hajamohideen, F. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10192-x · Cited by 195 · see citing works

Deep learning for Alzheimer's disease diagnosis: A survey

Khojaste-Sarakhsi, M., Haghighi, S. S., Ghomi, S. M. T. F., & Marchiori, E. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102332 · Cited by 194 · see citing works

Spatiotemporal Inconsistency Learning for DeepFake Video Detection

Gu, Z., Chen, Y., Yao, T., Ding, S., Li, J., Huang, F., & Ma, L. (2021) · Proceedings of the 29th ACM International Conference on Multimedia · doi:10.1145/3474085.3475508 · Cited by 189 · see citing works

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects

Koçak, B., Ponsiglione, A., Stanzione, A., Bluethgen, C., Santinha, J., Ugga, L., Huisman, M., Klontzas, M. E., Cannella, R., & Cuocolo, R. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242854 · Cited by 168 · see citing works

Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut

Wang, Y., Shen, X., Hu, S. X., Yuan, Y., Crowley, J. L., & Vaufreydaz, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01414 · Cited by 155 · see citing works

NAGNN: Classification of COVID-19 based on neighboring aware representation from deep graph neural network

Lu, S., Zhu, Z., Gorriz, J. M., Wang, S., & Zhang, Y. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22686 · Cited by 151 · see citing works

Quality control stress test for deep learning-based diagnostic model in digital pathology

Schömig-Markiefka, B., Pryalukhin, A., Hulla, W., Bychkov, A., Fukuoka, J., Madabhushi, A., Achter, V., Nieroda, L., Büttner, R., Quaas, A., et al. (2021) · Modern Pathology · doi:10.1038/s41379-021-00859-x · Cited by 146 · see citing works

Grounded Human-Object Interaction Hotspots From Video

Nagarajan, T., Feichtenhofer, C., & Grauman, K. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00878 · Cited by 147 · see citing works

A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer

Amgad, M., Hodge, J. M., Elsebaie, M. A. T., Bodelon, C., Puvanesarajah, S., Gutman, D. A., Siziopikou, K. P., Goldstein, J. A., Gaudet, M. M., Teras, L. R., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02643-7 · Cited by 137 · see citing works

An Evaluation of the Human-Interpretability of Explanation

Lage, I., Chen, E., He, J., Narayanan, M., Kim, B., Gershman, S., & Doshi-Velez, F. (2019) · arXiv · doi:10.48550/arxiv.1902.00006 · Cited by 125 · see citing works

RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions

Wang, Y., Shen, S., & Lim, B. Y. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581402 · Cited by 120 · see citing works

Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics

Jiang, Y., Zhou, K., Sun, Z., Wang, H., Xie, J., Zhang, T., Sang, S., Islam, M. T., Wang, J.-Y., Chen, C., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101146 · Cited by 120 · see citing works

Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based

McCoy, L. G., Brenna, C. T. A., Chen, S. S., Vold, K., & Das, S. (2022) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.11.001 · Cited by 118 · see citing works

Unsupervised Image Anomaly Detection and Segmentation Based on Pretrained Feature Mapping

Wan, Q., Gao, L., Li, X., & Wen, L. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3182385 · Cited by 120 · see citing works

Multimodal Deep Learning Approaches for Precision Oncology: A Comprehensive Review

Yang, H., Yang, M., Chen, J., Yao, G., Zou, Q., & Jia, L. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae699 · Cited by 115 · see citing works

Toward Trustworthy Artificial Intelligence (TAI) in the Context of Explainability and Robustness

chander, B., John, C., Warrier, L., & Gopalakrishnan, K. (2025) · ACM Computing Surveys · doi:10.1145/3675392 · Cited by 108 · see citing works

Perspectives on Individual Animal Identification from Biology and Computer Vision

Vidal, M., Wolf, N., Rosenberg, B., Harris, B. P., & Mathis, A. (2021) · Integrative and Comparative Biology · doi:10.1093/icb/icab107 · Cited by 104 · see citing works

Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face Augmentation

Das, S., Seferbekov, S., Datta, A., Islam, M. S., & Amin, M. R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw54120.2021.00421 · Cited by 107 · see citing works

Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy: a review

Lim, G., Bellemo, V., Xie, Y., Lee, X. Q., Yip, M. Y. T., & Ting, D. S. W. (2020) · Eye and Vision · doi:10.1186/s40662-020-00182-7 · Cited by 96 · see citing works

DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection

Yan, Z., Zhang, Y., Yuan, X., Lyu, S., & Wu, B. (2023) · arXiv · doi:10.48550/arxiv.2307.01426 · Cited by 97 · see citing works

Quantitative evaluation of explainable graph neural networks for molecular property prediction

Rao, J., Zheng, S., Lu, Y., & Yang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100628 · Cited by 90 · see citing works

A systematic review on the use of explainability in deep learning systems for computer aided diagnosis in radiology: Limited use of explainable AI?

Groen, A. M., Kraan, R., Amirkhan, S. F., Daams, J. G., & Maas, M. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110592 · Cited by 89 · see citing works

Benchmarking and scaling of deep learning models for land cover image classification

Papoutsis, I., Bountos, N. I., Zavras, A., Michail, D., & Tryfonopoulos, C. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.11.012 · Cited by 86 · see citing works

Enhancing histopathological medical image classification for Early cancer diagnosis using deep learning and explainable AI – LIME & SHAP

Ukwuoma, C. C., Cai, D., Eziefuna, E. O., Oluwasanmi, A., Abdi, S. F., Muoka, G. W., Thomas, D., & Sarpong, K. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107014 · Cited by 83 · see citing works

Explainable goal-driven agents and robots-a comprehensive review

Sado, F., Loo, C. K., Liew, W. S., Kerzel, M., & Wermter, S. (2023) · ACM Computing Surveys · doi:10.1145/3564240 · Cited by 79 · see citing works

Machine learning methods for histopathological image analysis: updates in 2024

Komura, D., Ochi, M., & Ishikawa, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.033 · Cited by 76 · see citing works

Psychological foundations of explainability and interpretability in artificial intelligence

Broniatowski, D. A. (2021) · doi:10.6028/nist.ir.8367 · Cited by 76 · see citing works

Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection

Hsu, C.-Y., & Li, W. (2023) · International Journal of Geographical Information Science · doi:10.1080/13658816.2023.2191256 · Cited by 75 · see citing works

Machine learning based medical image deepfake detection: A comparative study

Solaiyappan, S., & Wen, Y. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100298 · Cited by 73 · see citing works

Boosting Fast Adversarial Training With Learnable Adversarial Initialization

Jia, X., Zhang, Y., Wu, B., Wang, J., & Cao, X. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3184255 · Cited by 69 · see citing works

The silent trial - the bridge between bench-to-bedside clinical AI applications

Kwong, J. C. C., Erdman, L., Khondker, A., Skreta, M., Goldenberg, A., McCradden, M. D., Lorenzo, A. J., & Rickard, M. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.929508 · Cited by 73 · see citing works

Benchmarking foundation models as feature extractors for weakly supervised computational pathology

Neidlinger, P., El Nahhas, O. S. M., Muti, H. S., Lenz, T., Hoffmeister, M., Brenner, H., van Treeck, M., Langer, R., Dislich, B., Behrens, H. M., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01516-3 · Cited by 80 · see citing works

A review of visualisation-as-explanation techniques for convolutional neural networks and their evaluation

Mohamed, E., Sirlantzis, K., & Howells, G. (2022) · Displays · doi:10.1016/j.displa.2022.102239 · Cited by 67 · see citing works

Explainable Deep-Learning-Assisted Sweat Assessment via a Programmable Colorimetric Chip

Liu, Z., Li, J., Li, J., Yang, T., Zhang, Z., Wu, H., Xu, H., Meng, J., & Li, F. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.2c03927 · Cited by 59 · see citing works

Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study

Jiang, Y., Liang, X., Han, Z., Wang, W., Xi, S., Li, T., Chen, C., Yuan, Q., Li, N., Yu, J., et al. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00065-0 · Cited by 58 · see citing works

Decoding the Black Box: Explainable AI (XAI) for Cancer Diagnosis, Prognosis, and Treatment Planning—A State-of-the-Art Systematic Review

Abas Mohamed, Y., Ee Khoo, B., Shahrimie Mohd Asaari, M., Ezane Aziz, M., & Rahiman Ghazali, F. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105689 · Cited by 57 · see citing works

OpenSoundscape: An open‐source bioacoustics analysis package for Python

Lapp, S., Rhinehart, T., Freeland‐Haynes, L., Khilnani, J., Syunkova, A., & Kitzes, J. (2023) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14196 · Cited by 57 · see citing works

Deepfake Detection: Analyzing Model Generalization Across Architectures, Datasets, and Pre-Training Paradigms

Khan, S. A., & Dang-Nguyen, D.-T. (2024) · IEEE Access · doi:10.1109/access.2023.3348450 · Cited by 53 · see citing works

Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery

Wagner, P., Mehari, T., Haverkamp, W., & Strodthoff, N. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108525 · Cited by 48 · see citing works

Hybrid-COVID: a novel hybrid 2D/3D CNN based on cross-domain adaptation approach for COVID-19 screening from chest X-ray images

Bayoudh, K., Hamdaoui, F., & Mtibaa, A. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00957-1 · Cited by 50 · see citing works

A review of explainable AI techniques and their evaluation in mammography for breast cancer screening

Shifa, N., Saleh, M., Akbari, Y., & Al Maadeed, S. (2025) · Clinical Imaging · doi:10.1016/j.clinimag.2025.110492 · Cited by 45 · see citing works

Diagnosis of Early Glottic Cancer Using Laryngeal Image and Voice Based on Ensemble Learning of Convolutional Neural Network Classifiers

Kwon, I., Wang, S.-G., Shin, S.-C., Cheon, Y.-I., Lee, B.-J., Lee, J.-C., Lim, D.-W., Jo, C., Cho, Y., & Shin, B.-J. (2025) · Journal of Voice · doi:10.1016/j.jvoice.2022.07.007 · Cited by 43 · see citing works

A Collaborative Federated Learning Framework for Lung and Colon Cancer Classifications

Hossain, M. M., Islam, M. R., Ahamed, M. F., Ahsan, M., & Haider, J. (2024) · Technologies · doi:10.3390/technologies12090151 · Cited by 44 · see citing works

Explainability and AI Confidence in Clinical Decision Support Systems: Effects on Trust, Diagnostic Performance, and Cognitive Load in Breast Cancer Care

Rezaeian, O., Bayrak, A. E., & Asan, O. (2026) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2025.2539458 · Cited by 39 · see citing works

Explainability and AI confidence in clinical decision support systems: effects on trust, diagnostic performance, and cognitive load in breast Cancer care

Rezaeian, O., Bayrak, A. E., & Asan, O. (2025) · arXiv · doi:10.48550/arxiv.2501.16693 · Cited by 39 · see citing works

Towards successful implementation of artificial intelligence in skin cancer care: a qualitative study exploring the views of dermatologists and general practitioners

Sangers, T. E., Wakkee, M., Moolenburgh, F. J., Nijsten, T., & Lugtenberg, M. (2022) · Archives of Dermatological Research · doi:10.1007/s00403-022-02492-3 · Cited by 38 · see citing works

Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024

Žigutytė, L., Sorz-Nechay, T., Clusmann, J., & Kather, J. N. (2024) · JHEP Reports · doi:10.1016/j.jhepr.2024.101209 · Cited by 33 · see citing works

Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester

Walker, M. C., Willner, I., Miguel, O. X., Murphy, M. S. Q., El-Chaâr, D., Moretti, F., Dingwall Harvey, A. L. J., Rennicks White, R., Muldoon, K. A., Carrington, A. M., et al. (2022) · PLOS ONE · doi:10.1371/journal.pone.0269323 · Cited by 31 · see citing works

Deep-Learning Model for Screening Sepsis Using Electrocardiography

Kwon, J.-M., Lee, Y. R., Jung, M.-S., Lee, Y.-J., Jo, Y.-Y., Kang, D.-Y., Lee, S. Y., Cho, Y.-H., Shin, J.-H., Ban, J.-H., et al. (2021) · Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine · doi:10.1186/s13049-021-00953-8 · Cited by 28 · see citing works

AI Advances in ICU with an Emphasis on Sepsis Prediction: An Overview

Stylianides, C., Nicolaou, A., Sulaiman, W. A., Alexandropoulou, C.-A., Panagiotopoulos, I., Karathanasopoulou, K., Dimitrakopoulos, G., Kleanthous, S., Politi, E., Ntalaperas, D., et al. (2025) · Machine Learning and Knowledge Extraction · doi:10.3390/make7010006 · Cited by 28 · see citing works

Extendable and explainable deep learning for pan-cancer radiogenomics research

Liu, Q., & Hu, P. (2022) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.102111 · Cited by 27 · see citing works

DeepGA for automatically estimating fetal gestational age through ultrasound imaging

Dan, T., Chen, X., He, M., Guo, H., He, X., Chen, J., Xian, J., Hu, Y., Zhang, B., Wang, N., et al. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102453 · Cited by 25 · see citing works

Coronary calcium detection using 3D attention identical dual deep network based on weakly supervised learning

Huo, Y., Terry, J. G., Wang, J., Nath, V., Bermudez, C., Bao, S., Parvathaneni, P., Carr, J. J., & Landman, B. A. (2019) · Medical Imaging 2019: Image Processing · doi:10.1117/12.2512541 · Cited by 22 · see citing works

Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection

Hou, L., Zhang, Y., Fu, K., & Li, J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00980 · Cited by 19 · see citing works

How Should AI Decisions Be Explained? Requirements for Explanations from the Perspective of European Law

Fresz, B., Dubovitskaya, E., Brajovic, D., Huber, M. F., & Horz, C. (2024) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1609/aies.v7i1.31648 · Cited by 17 · see citing works

Automated pharyngeal phase detection and bolus localization in videofluoroscopic swallowing study: killing two birds with one stone?

Bandini, A., Smaoui, S., & Steele, C. M. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107058 · Cited by 16 · see citing works

Lessons learned from RadiologyNET foundation models for transfer learning in medical radiology

Napravnik, M., Hržić, F., Urschler, M., Miletić, D., & Štajduhar, I. (2025) · Scientific Reports · doi:10.1038/s41598-025-05009-w · Cited by 15 · see citing works

The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases

Tang, J., Han, J., Xie, B., Xue, J., Zhou, H., Jiang, Y., Hu, L., Chen, C., Zhang, K., Zhu, F., et al. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20032377 · Cited by 13 · see citing works

ISP Meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing

Santos, C. F. G. D., Arrais, R. R., Silva, J. V. S. D., Silva, M. H. M. D., Neto, W. B. G. D. A., Lopes, L. T., Bileki, G. A., Lima, I. O., Rondon, L. B., Souza, B. M. D., et al. (2025) · ACM Computing Surveys · doi:10.1145/3708516 · Cited by 9 · see citing works

From Black Boxes to Glass Boxes: Explainable AI for Trustworthy Deepfake Forensics

Qian, H., Xia, L., Ge, R., Fan, Y., Wang, Q., & Jing, Z. (2025) · Cryptography · doi:10.3390/cryptography9040061 · Cited by 10 · see citing works

Explainable deep-learning-based ischemia detection using hybrid O-15 H2O perfusion positron emission tomography and computed tomography imaging with clinical data

Teuho, J., Schultz, J., Klén, R., Juarez-Orozco, L. E., Knuuti, J., Saraste, A., Ono, N., & Kanaya, S. (2024) · Journal of Nuclear Cardiology · doi:10.1016/j.nuclcard.2024.101889 · Cited by 7 · see citing works

EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge

Palash, M., & Bhargava, B. (2023) · arXiv · doi:10.48550/arxiv.2306.08657 · Cited by 25 · see citing works

Motor imagery EEG classification via wavelet-packet synthetic augmentation and entropy-based channel selection

Zheng, M., Qian, Z., & Zhao, T. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1689647 · Cited by 1 · see citing works

A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification

Barki, C., Chouchen, M., Sediri, A., Ceylan, H. İ., Rahmouni, H. B., Muntean, R. I., El-Seedi, H. R., Bragazzi, N. L., & Dergaa, I. (2026) · Frontiers in Systems Biology · doi:10.3389/fsysb.2026.1892418 · Cited by 1 · see citing works

Deep Learning vs. Traditional Machine Learning in the Diagnosis of Cutaneous Drug Eruptions: A Comparative Study with Explainable AI on Whole-Slide Images

Türkmen Dedeoğlu, A., Yanık, H., Uysal, E., Fikir, G., Değirmenci, E., Türsen, Ü., & Yuyucu Karabulut, Y. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02057-0

Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review

Ebraheem, M., Toghranegar, J., Bridge2AI-Voice Consortium, Bensoussan, Y., & Templeton, J. M. (2026) · Journal of Medical Internet Research · doi:10.2196/83790