Transformer Interpretability Beyond Attention Visualization

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works

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

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513 · see citing works

A Unified Approach to Interpreting Model Predictions

Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 21,793 · see citing works

End-to-End Object Detection with Transformers

Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285 · see citing works

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515 · 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

On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)

Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623 · 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

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248 · 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

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524 · see citing works

Axiomatic attribution for deep networks

Sundararajan, M., Taly, A., & Yan, Q. (2017) · arXiv · doi:10.48550/arxiv.1703.01365 · Cited by 4,087 · see citing works

Gaussian Error Linear Units (GELUs)

Hendrycks, D., & Gimpel, K. (2016) · arXiv · doi:10.48550/arxiv.1606.08415 · Cited by 3,537 · see citing works

Learning Important Features Through Propagating Activation Differences

Shrikumar, A., Greenside, P., & Kundaje, A. (2017) · arXiv · doi:10.48550/arxiv.1704.02685 · Cited by 2,761 · see citing works

LXMERT: Learning Cross-Modality Encoder Representations from Transformers

Tan, H., & Bansal, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1514 · Cited by 2,319 · see citing works

ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

Lu, J., Batra, D., Parikh, D., & Lee, S. (2019) · arXiv · doi:10.48550/arxiv.1908.02265 · Cited by 2,109 · see citing works

SmoothGrad: removing noise by adding noise

Smilkov, D., Thorat, N., Kim, B., Viégas, F., & Wattenberg, M. (2017) · arXiv · doi:10.48550/arxiv.1706.03825 · Cited by 1,467 · see citing works

A Decomposable Attention Model for Natural Language Inference

Parikh, A. P., Täckström, O., Das, D., & Uszkoreit, J. (2016) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1244 · Cited by 1,453 · see citing works

Explaining nonlinear classification decisions with deep taylor decomposition

Montavon, G., Lapuschkin, S., Binder, A., Samek, W., & Müller, K.-R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.11.008 · Cited by 1,357 · see citing works

Long Short-Term Memory-Networks for Machine Reading

Cheng, J.-P., Dong, L., & Lapata, M. (2016) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1053 · Cited by 1,200 · see citing works

Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Voita, E., Talbot, D., Moiseev, F., Sennrich, R., & Titov, I. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1580 · Cited by 1,146 · see citing works

Interpretable Explanations of Black Boxes by Meaningful Perturbation

Fong, R. C., & Vedaldi, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.371 · Cited by 1,097 · see citing works

Untitled work

(2020) · International Conference on Machine Learning · Cited by 1,012 · see citing works

Quantifying Attention Flow in Transformers

Abnar, S., & Zuidema, W. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.385 · Cited by 995 · see citing works

VL-BERT: Pre-training of Generic Visual-Linguistic Representations

Su, W., Zhu, X., Cao, Y., Li, B., Lu, L., Wei, F., & Dai, J. (2019) · arXiv · doi:10.48550/arxiv.1908.08530 · Cited by 950 · see citing works

Explaining Explanations in AI

Mittelstadt, B., Russell, C., & Wachter, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287574 · Cited by 794 · see citing works

Top-Down Neural Attention by Excitation Backprop

Zhang, J., Bargal, S. A., Lin, Z., Brandt, J., Shen, X., & Sclaroff, S. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1059-x · Cited by 769 · see citing works

Not Just a Black Box: Learning Important Features Through Propagating Activation DifferencesPreprint

Shrikumar, A., Greenside, P., Shcherbina, A., & Kundaje, A. B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.01713 · Cited by 704 · see citing works

ERASER: A Benchmark to Evaluate Rationalized NLP Models

DeYoung, J., Jain, S., Rajani, N. F., Lehman, E., Xiong, C., Socher, R., & Wallace, B. C. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.408 · Cited by 561 · see citing works

Tell Me Where to Look: Guided Attention Inference Network

Li, K., Wu, Z., Peng, K., Ernst, J., & Fu, Y. (2018) · 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.2018.00960 · Cited by 546 · 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

Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers

Binder, A., Montavon, G., Lapuschkin, S., Müller, K., & Samek, W. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44781-0_8 · Cited by 455 · see citing works

A Benchmark for Interpretability Methods in Deep Neural NetworksPreprint

Hooker, S., Erhan, D., Kindermans, P.-J., & Kim, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.10758 · Cited by 438 · see citing works

Real Time Image Saliency for Black Box ClassifiersPreprint

Dabkowski, P., & Gal, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.07857 · Cited by 404 · see citing works

Interpreting Deep Visual Representations via Network Dissection

Zhou, B., Bau, D., Oliva, A., & Torralba, A. B. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2858759 · Cited by 334 · see citing works

VL-BERT: Pre-training of Generic Visual-Linguistic Representations

Su, W., Zhu, X., Cao, Y., Li, B., Lu, L., Wei, F., & Dai, J. (2020) · International Conference on Learning Representations · Cited by 276 · see citing works

ImageNet Auto-Annotation with Segmentation Propagation

Guillaumin, M., Kuettel, D., & Ferrari, V. (2014) · International Journal of Computer Vision · doi:10.1007/s11263-014-0713-9 · Cited by 131 · see citing works

Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance Propagation

Iwana, B. K., Kuroki, R., & Uchida, S. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00513 · Cited by 115 · see citing works

Modeling annotators

Zaidan, O. F., & Eisner, J. M. (2008) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1613715.1613721 · Cited by 109 · see citing works

L-Shapley and C-Shapley: Efficient Model Interpretation for Structured DataPreprint

Chen, J., Song, L., Wainwright, M. J., & Jordan, M. I. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.02610 · Cited by 108 · see citing works

Understanding Individual Decisions of CNNs via Contrastive Backpropagation

Gu, J., Yang, Y., & Tresp, V. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20893-6_8 · Cited by 101 · see citing works

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 · see citing works

Full-Gradient Representation for Neural Network VisualizationPreprint

Srinivas, S., & Fleuret, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.00780 · Cited by 72 · see citing works

Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided Factorization

Gur, S., Ali, A., & Wolf, L. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i13.17374 · Cited by 34 · see citing works

Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided Factorization

Gur, S., Ali, A., & Wolf, L. (2020) · arXiv · doi:10.48550/arxiv.2012.02166 · Cited by 34 · see citing works

Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance PropagationPreprint

Iwana, B. K., Kuroki, R., & Uchida, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.04351 · Cited by 20 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805

Untitled work

Han (2023) · doi:10.1109/tpami.2022.3152247 · Cited by 4,682 · see citing works

Transformers in Vision: A Survey

Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., & Shah, M. (2022) · ACM Computing Surveys · doi:10.1145/3505244 · Cited by 3,595 · see citing works

Transformers in medical imaging: A survey

Shamshad, F., Khan, S., Zamir, S. W., Khan, M. H., Hayat, M., Khan, F. S., & Fu, H. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102802 · Cited by 1,412 · see citing works

A foundation model for generalizable disease detection from retinal images

Zhou, Y., Chia, M. A., Wagner, S. K., Ayhan, M. S., Williamson, D. J., Struyven, R. R., Liu, T., Xu, M., Lozano, M. G., Woodward-Court, P., et al. (2023) · Nature · doi:10.1038/s41586-023-06555-x · Cited by 1,014 · see citing works

Explainability for Large Language Models: A Survey

Zhao, H., Chen, H., Yang, F., Liu, N., Deng, H., Cai, H., Wang, S., Yin, D., & Du, M. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3639372 · Cited by 681 · see citing works

Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond

Li, X., Xiong, H., Li, X., Wu, X., Zhang, X., Liu, J., Bian, J., & Dou, D. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01756-8 · Cited by 542 · see citing works

Advances in medical image analysis with vision Transformers: A comprehensive review

Azad, R., Kazerouni, A., Heidari, M., Aghdam, E. K., Molaei, A., Jia, Y., Jose, A., Roy, R., & Merhof, D. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103000 · Cited by 523 · see citing works

TransPose: Keypoint Localization via Transformer

Yang, S., Quan, Z., Nie, M., & Yang, W. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01159 · Cited by 449 · see citing works

Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives

Li, J., Chen, J., Tang, Y., Wang, C., Landman, B. A., & Zhou, S. K. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102762 · Cited by 426 · see citing works

TransFER: Learning Relation-aware Facial Expression Representations with Transformers

Xue, F., Wang, Q., & Guo, G. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00358 · Cited by 344 · see citing works

CLIP-ReID: Exploiting Vision-Language Model for Image Re-identification without Concrete Text Labels

Li, S., Li, S., & Li, Q. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25225 · Cited by 335 · see citing works

DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification

Rao, Y., Zhao, W., Liu, B., Lu, J., Zhou, J., & Hsieh, C.-J. (2021) · arXiv · doi:10.48550/arxiv.2106.02034 · Cited by 448 · see citing works

Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers

Chefer, H., Gur, S., & Wolf, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00045 · Cited by 313 · see citing works

CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising

Wang, D., Fan, F.-L., Wu, Z., Liu, R., Wang, F., & Yu, H. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acc000 · Cited by 241 · see citing works

A Graph-Transformer for Whole Slide Image Classification

Zheng, Y., Gindra, R. H., Green, E. J., Burks, E. J., Betke, M., Beane, J. E., & Kolachalama, V. B. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3176598 · Cited by 255 · see citing works

Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction

Mersha, M. A., Lâm, K. N., Wood, J., AlShami, A. K., & Kalita, J. K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128111 · Cited by 301 · see citing works

Survey on Explainable AI: From Approaches, Limitations and Applications Aspects

Yang, W., Wei, Y.-C., Wei, H., Chen, Y., Huang, G., Li, X., Li, R., Yao, N., Wang, X., Gu, X., et al. (2023) · Human-Centric Intelligent Systems · doi:10.1007/s44230-023-00038-y · Cited by 275 · see citing works

Machine learning in point-of-care testing: innovations, challenges, and opportunities

Han, G., Goncharov, A., Eryılmaz, M., Ye, S., Palanisamy, B., Ghosh, R., Lisi, F., Rogers, E., Guzmán, D., Yigci, D., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58527-6 · Cited by 260 · see citing works

Transformers and large language models in healthcare: A review

Nerella, S., Bandyopadhyay, S., Zhang, J., Contreras, M., Siegel, S., Bumin, A., Silva, B., Sena, J., Shickel, B., Bihorac, A., et al. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102900 · Cited by 243 · see citing works

Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer

Xu, Y., Zhang, Z., Zhang, M., Sheng, K., Li, K., Dong, W., Zhang, L., Xu, C., & Sun, X. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20202 · Cited by 202 · see citing works

Transformer-based deep learning for predicting protein properties in the life sciences

Chandra, A. A., Tünnermann, L., Löfstedt, T., & Gratz, R. (2023) · eLife · doi:10.7554/elife.82819 · Cited by 192 · see citing works

ISTVT: Interpretable Spatial-Temporal Video Transformer for Deepfake Detection

Zhao, C., Wang, C., Hu, G., Chen, H., Liu, C., & Tang, J. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3239223 · Cited by 184 · see citing works

CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation

Gadre, S. Y., Wortsman, M., Ilharco, G., Schmidt, L., & Song, S. (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/cvpr52729.2023.02219 · Cited by 192 · see citing works

SPViT: Enabling Faster Vision Transformers via Latency-Aware Soft Token Pruning

Kong, Z., Dong, P., Ma, X., Meng, X., Niu, W., Sun, M., Shen, X., Yuan, G., Ren, B., Tang, H., et al. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20083-0_37 · Cited by 169 · see citing works

Transforming Conversations with AI—A Comprehensive Study of ChatGPT

Bansal, G., Chamola, V., Hussain, A., Guizani, M., & Niyato, D. T. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10236-2 · Cited by 190 · see citing works

POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition

Zheng, C., Mendieta, M., & Chen, C. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00339 · Cited by 168 · see citing works

SpaText: Spatio-Textual Representation for Controllable Image Generation

Avrahami, O., Hayes, T., Gafni, O., Gupta, S., Taigman, Y., Parikh, D., Lischinski, D., Fried, O., & Yin, X. (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/cvpr52729.2023.01762 · Cited by 171 · see citing works

Vision Transformer With Attentive Pooling for Robust Facial Expression Recognition

Xue, F., Wang, Q., Tan, Z., Ma, Z., & Guo, G. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3226473 · Cited by 166 · see citing works

YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms

Su, Y., Liu, Q., Xie, W., & Hu, P. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106903 · Cited by 161 · see citing works

A transformer-based weakly supervised computational pathology method for clinical-grade diagnosis and molecular marker discovery of gliomas

Jiang, R., Yin, X., Yang, P., Cheng, L., Hu, J., Jiao, Y., Wang, Y., Fu, X., Shang, L., Li, L., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00868-w · Cited by 160 · see citing works

Interpretable machine learning for dementia: a systematic review

Martin, S. A., Townend, F. J., Barkhof, F., & Cole, J. H. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.12948 · Cited by 149 · see citing works

Deconv-transformer (DecT): A histopathological image classification model for breast cancer based on color deconvolution and transformer architecture

Zhu, H., Lin, M., Xu, Z., Yao, Z., Chen, H., Alhudhaif, A., & Alenezi, F. S. (2022) · Information Sciences · doi:10.1016/j.ins.2022.06.091 · Cited by 142 · see citing works

Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications

Abibullaev, B., Keutayeva, A., & Zollanvari, A. (2023) · IEEE Access · doi:10.1109/access.2023.3329678 · Cited by 147 · see citing works

KVT: k-NN Attention for Boosting Vision Transformers

Wang, P., Wang, X., Wang, F., Lin, M., Chang, S., Li, H., & Jin, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20053-3_17 · Cited by 142 · see citing works

Transformers in computational visual media: A survey

Xu, Y., Wei, H., Lin, M., Deng, Y., Sheng, K., Zhang, M., Tang, F., Dong, W., Huang, F., & Xu, C. (2021) · Computational Visual Media · doi:10.1007/s41095-021-0247-3 · Cited by 137 · see citing works

xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography

Mondal, A. K., Bhattacharjee, A., Singla, P., & Prathosh, A. P. (2021) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2021.3134096 · Cited by 135 · see citing works

Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review

Coan, L. J., Williams, B. M., Adithya, V. K., Upadhyaya, S., Kafri, A. S. A., Czanner, S., Venkatesh, R., Willoughby, C. E., Kavitha, S., & Czanner, G. (2022) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2022.08.005 · Cited by 132 · see citing works

M3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer

Jang, J., & Hwang, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02006 · Cited by 133 · see citing works

XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process

Clement, T., Kemmerzell, N., Abdelaal, M., & Amberg, M. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5010006 · Cited by 131 · see citing works

Towards Faithful Model Explanation in NLP: A Survey

Lyu, Q., Apidianaki, M., & Callison-Burch, C. (2024) · Computational Linguistics · doi:10.1162/coli_a_00511 · Cited by 143 · see citing works

On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing

George, A., & Marcel, S. (2021) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb52358.2021.9484333 · Cited by 127 · see citing works

EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications

Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024) · IEEE Access · doi:10.1109/access.2024.3409843 · Cited by 124 · see citing works

Multi-task vision transformer using low-level chest X-ray feature corpus for COVID-19 diagnosis and severity quantification

Park, S. J., Kim, G., Oh, Y., Seo, J. B., Lee, S. M., Kim, J. H., Moon, S.-J., Lim, J., & Ye, J. C. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102299 · Cited by 120 · see citing works

Deep Learning for Exploring Landslides with Remote Sensing and Geo-Environmental Data: Frameworks, Progress, Challenges, and Opportunities

Zhang, Q., & Wang, T. (2024) · Remote Sensing · doi:10.3390/rs16081344 · Cited by 127 · see citing works

A closer look at the explainability of Contrastive language-image pre-training

Li, Y., Wang, H., Duan, Y., Zhang, J., & Li, X. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111409 · Cited by 116 · see citing works

A Comprehensive Survey of Transformers for Computer Vision

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Hierarchical Frequency-Assisted Interactive Networks for Face Manipulation Detection

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