2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models

Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115

Performance of ChatGPT on USMLE: Potential for AI-Assisted Medical Education Using Large Language Models

Kung, T. H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., et al. (2022) · doi:10.1101/2022.12.19.22283643 · Cited by 4,115

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3703155 · Cited by 4,688

Deep Networks with Stochastic Depth

Huang, G., Sun, Y., Liu, Z., Sedra, D., & Weinberger, K. Q. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_39 · Cited by 2,104

Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

Garg, R., B.G., V. K., Carneiro, G., & Reid, I. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_45 · Cited by 1,632

Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930

Is Faster R-CNN Doing Well for Pedestrian Detection?

Zhang, L., Lin, L., Liang, X., & He, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_28 · Cited by 852

Artificial intelligence in retina

Schmidt-Erfurth, U., Sadeghipour, A., Gerendas, B. S., Waldstein, S. M., & Bogunović, H. (2018) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2018.07.004 · Cited by 779

Artificial intelligence in the creative industries: a review

Anantrasirichai, N., & Bull, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10039-7 · Cited by 793

Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward

Masood, M., Nawaz, M., Malik, K. M., Javed, A., Irtaza, A., & Malik, H. (2023) · Applied Intelligence · doi:10.1007/s10489-022-03766-z · Cited by 543

Transparency of deep neural networks for medical image analysis: A review of interpretability methods

Salahuddin, Z., Woodruff, H. C., Chatterjee, A., & Lambin, P. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105111 · Cited by 508

Automated image-based tracking and its application in ecology

Dell, A. I., Bender, J. A., Branson, K., Couzin, I. D., de Polavieja, G. G., Noldus, L. P. J. J., Pérez-Escudero, A., Perona, P., Straw, A. D., Wikelski, M., et al. (2014) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2014.05.004 · Cited by 492

Explainable artificial intelligence model to predict acute critical illness from electronic health records

Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020) · Nature Communications · doi:10.1038/s41467-020-17431-x · Cited by 448

Metabolomic profiles predict individual multidisease outcomes

Buergel, T., Steinfeldt, J., Ruyoga, G., Pietzner, M., Bizzarri, D., Vojinovic, D., Upmeier zu Belzen, J., Loock, L., Kittner, P., Christmann, L., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01980-3 · Cited by 406

Deep learning in bioinformatics: Introduction, application, and perspective in the big data era

Li, Y., Huang, C., Ding, L., Li, Z., Pan, Y., & Gao, X. (2019) · Methods · doi:10.1016/j.ymeth.2019.04.008 · Cited by 401

How to build the virtual cell with artificial intelligence: Priorities and opportunities

Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.015 · Cited by 393

A review on modern defect detection models using DCNNs – Deep convolutional neural networks

Tulbure, A.-A., Tulbure, A.-A., & Dulf, E.-H. (2022) · Journal of Advanced Research · doi:10.1016/j.jare.2021.03.015 · Cited by 372

Predicting conversion to wet age-related macular degeneration using deep learning

Yim, J., Chopra, R., Spitz, T., Winkens, J., Obika, A., Kelly, C., Askham, H., Lukic, M., Huemer, J., Fasler, K., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0867-7 · Cited by 297

Segmental Spatiotemporal CNNs for Fine-Grained Action Segmentation

Lea, C., Reiter, A., Vidal, R., & Hager, G. D. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46487-9_3 · Cited by 246

Deep Network Cascade for Image Super-resolution

Cui, Z., Chang, H., Shan, S., Zhong, B., & Chen, X. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_4 · Cited by 255

AFEW-VA database for valence and arousal estimation in-the-wild

Kossaifi, J., Tzimiropoulos, G., Todorovic, S., & Pantic, M. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.02.001 · Cited by 236

Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)

Seoni, S., Jahmunah, V., Salvi, M., Barua, P. D., Molinari, F., & Acharya, U. R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107441 · Cited by 239

What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?

Mayer, N., Ilg, E., Fischer, P., Hazirbas, C., Cremers, D., Dosovitskiy, A., & Brox, T. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1082-6 · Cited by 191

Effects of Explainable Artificial Intelligence on trust and human behavior in a high-risk decision task

Leichtmann, B., Humer, C., Hinterreiter, A., Streit, M., & Mara, M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107539 · Cited by 200

Photometric redshifts from SDSS images using a convolutional neural network

Pasquet, J., Bertin, E., Treyer, M., Arnouts, S., & Fouchez, D. (2019) · Astronomy & Astrophysics · doi:10.1051/0004-6361/201833617 · Cited by 197

Filter-based deep-compression with global average pooling for convolutional networks

Hsiao, T.-Y., Chang, Y.-C., Chou, H.-H., & Chiu, C.-T. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.02.008 · Cited by 190

Collaboration between clinicians and vision–language models in radiology report generation

Tanno, R., Barrett, D. G. T., Sellergren, A., Ghaisas, S., Dathathri, S., See, A., Welbl, J., Lau, C., Tu, T., Azizi, S., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03302-1 · Cited by 186

Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information

Kim, B., Park, J., & Suh, J. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113302 · Cited by 182

Medical image registration using deep neural networks: A comprehensive review

Boveiri, H. R., Khayami, R., Javidan, R., & Mehdizadeh, A. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106767 · Cited by 179

How far have we come? Artificial intelligence for chest radiograph interpretation

Kallianos, K., Mongan, J., Antani, S., Henry, T., Taylor, A., Abuya, J., & Kohli, M. (2019) · Clinical Radiology · doi:10.1016/j.crad.2018.12.015 · Cited by 173

Spatio-temporal convolutional features with nested LSTM for facial expression recognition

Yu, Z., Liu, G., Liu, Q., & Deng, J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.07.028 · Cited by 162

Simultaneous single-and multi-contrast super-resolution for brain MRI images based on a convolutional neural network

Zeng, K., Zheng, H., Cai, C., Yang, Y., Zhang, K., & Chen, Z. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.06.010 · Cited by 161

Improving chest X-ray report generation by leveraging warm starting

Nicolson, A., Dowling, J., & Koopman, B. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102633 · Cited by 161

Untitled work

(2020) · doi:10.1007/978-3-030-58586-0_27 · Cited by 156

High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells

Kim, H. K., Lee, S., Kim, Y., Park, J., Min, S., Choi, J. W., Huang, T. P., Yoon, S., Liu, D. R., & Kim, H. H. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0505-1 · Cited by 145

Efficient reconstruction of nonrigid shape and motion from real-time 3D scanner data

Wand, M., Adams, B., Ovsjanikov, M., Berner, A., Bokeloh, M., Jenke, P., Guibas, L., Seidel, H.-P., & Schilling, A. (2009) · ACM Transactions on Graphics · doi:10.1145/1516522.1516526 · Cited by 136

An active contour model based on local fitted images for image segmentation

Wang, L., Chang, Y., Wang, H., Wu, Z., Pu, J., & Yang, X. (2017) · Information Sciences · doi:10.1016/j.ins.2017.06.042 · Cited by 127

Multiple kernel learning for emotion recognition in the wild

Sikka, K., Dykstra, K., Sathyanarayana, S., Littlewort, G., & Bartlett, M. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction · doi:10.1145/2522848.2531741 · Cited by 119

Fusion of range and color images for denoising and resolution enhancement with a non-local filter

Huhle, B., Schairer, T., Jenke, P., & Straßer, W. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2009.11.004 · Cited by 116

The effect of deep learning methods on deepfake audio detection for digital investigation

Mcuba, M., Singh, A., Ikuesan, R. A., & Venter, H. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.283 · Cited by 117

Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer

Li, F., Yang, Y., Wei, Y., He, P., Chen, J., Zheng, Z., & Bu, H. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-021-03020-z · Cited by 113

Structured layer surface segmentation for retina OCT using fully convolutional regression networks

He, Y., Carass, A., Liu, Y., Jedynak, B. M., Solomon, S. D., Saidha, S., Calabresi, P. A., & Prince, J. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101856 · Cited by 104

Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments

Adeel, A., Gogate, M., & Hussain, A. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.08.008 · Cited by 96

Learning and inference on generative adversarial quantum circuits

Zeng, J., Wu, Y., Liu, J.-G., Wang, L., & Hu, J. (2019) · Physical Review A · doi:10.1103/physreva.99.052306 · Cited by 94

A multimodal emotion recognition method based on facial expressions and electroencephalography

Tan, Y., Sun, Z., Duan, F., Solé-Casals, J., & Caiafa, C. F. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103029 · Cited by 88

General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance

Triguero, I., Molina, D., Poyatos, J., Del Ser, J., & Herrera, F. (2024) · Information Fusion · doi:10.1016/j.inffus.2023.102135 · Cited by 90

A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded

Ozyoruk, K. B., Can, S., Darbaz, B., Başak, K., Demir, D., Gokceler, G. I., Serin, G., Hacisalihoglu, U. P., Kurtuluş, E., Lu, M. Y., et al. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00952-9 · Cited by 86

Security in smart cities: A brief review of digital forensic schemes for biometric data

Ross, A., Banerjee, S., & Chowdhury, A. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.07.009 · Cited by 85

A multimodal convolutional neuro-fuzzy network for emotion understanding of movie clips

Nguyen, T.-L., Kavuri, S., & Lee, M. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.06.010 · Cited by 84

Digital twin-based intelligent fish farming with Artificial Intelligence Internet of Things (AIoT)

Ubina, N. A., Lan, H.-Y., Cheng, S.-C., Chang, C.-C., Lin, S.-S., Zhang, K.-X., Lu, H.-Y., Cheng, C.-Y., & Hsieh, Y.-Z. (2023) · Smart Agricultural Technology · doi:10.1016/j.atech.2023.100285 · Cited by 82

Understanding the onset of hot streaks across artistic, cultural, and scientific careers

Liu, L., Dehmamy, N., Chown, J., Giles, C. L., & Wang, D. (2021) · Nature Communications · doi:10.1038/s41467-021-25477-8 · Cited by 81

How deep is the brain? The shallow brain hypothesis

Suzuki, M., Pennartz, C. M. A., & Aru, J. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-023-00756-z · Cited by 79

Spontaneous dyadic behavior predicts the emergence of interpersonal neural synchrony

Koul, A., Ahmar, D., Iannetti, G. D., & Novembre, G. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120233 · Cited by 75

Biomechanically constrained non-rigid mr-trus prostate registration using deep learning based 3d point cloud matching

Fu, Y., Lei, Y., Wang, T., Patel, P., Jani, A. B., Mao, H., Curran, W. J., Liu, T., & Yang, X. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101845 · Cited by 75

Application of deep learning–based computer-aided detection system: detecting pneumothorax on chest radiograph after biopsy

Park, S., Lee, S. M., Kim, N., Choe, J., Cho, Y., Do, K.-H., & Seo, J. B. (2019) · European Radiology · doi:10.1007/s00330-019-06130-x · Cited by 71

Quantifying and alleviating political bias in language models

Liu, R., Jia, C., Wei, J., Xu, G., & Vosoughi, S. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2021.103654 · Cited by 68

Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification

Wollek, A., Graf, R., Čečatka, S., Fink, N., Willem, T., Sabel, B. O., & Lasser, T. (2023) · Radiology: Artificial Intelligence · doi:10.1148/ryai.220187 · Cited by 68

Optical navigation for Lunar landing based on Convolutional Neural Network crater detector

Silvestrini, S., Piccinin, M., Zanotti, G., Brandonisio, A., Bloise, I., Feruglio, L., Lunghi, P., Lavagna, M., & Varile, M. (2022) · Aerospace Science and Technology · doi:10.1016/j.ast.2022.107503 · Cited by 65

A survey of model compression strategies for object detection

Lyu, Z., Yu, T., Pan, F., Zhang, Y., Luo, J., Zhang, D., Chen, Y., Zhang, B., & Li, G. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17192-x · Cited by 65

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Dayoub, F., Cielniak, G., & Duckett, T. (2011) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2011.02.013 · Cited by 59

A multimodal fusion approach for image captioning

Zhao, D., Chang, Z., & Guo, S. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.11.004 · Cited by 61

STDnet-ST: Spatio-temporal ConvNet for small object detection

Bosquet, B., Mucientes, M., & Brea, V. M. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107929 · Cited by 57

Translation and rotation equivariant normalizing flow (TRENF) for optimal cosmological analysis

Dai, B., & Seljak, U. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac2010 · Cited by 48

Real-Time Automatic Assisted Detection of Uterine Fibroid in Ultrasound Images Using a Deep Learning Detector

Yang, T., Yuan, L., Li, P., & Liu, P. (2023) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2023.03.013 · Cited by 47

Protein p K a Prediction with Machine Learning

Cai, Z., Luo, F., Wang, Y., Li, E., & Huang, Y. (2021) · ACS Omega · doi:10.1021/acsomega.1c05440 · Cited by 45

Face mask detection in COVID-19: a strategic review

Vibhuti, Jindal, N., Singh, H., & Rana, P. S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12999-6 · Cited by 41

Probabilistic mass-mapping with neural score estimation

Remy, B., Lanusse, F., Jeffrey, N., Liu, J., Starck, J.-L., Osato, K., & Schrabback, T. (2023) · Astronomy & Astrophysics · doi:10.1051/0004-6361/202243054 · Cited by 41

Spectrum of advancements and developments in multidisciplinary domains for generative adversarial networks (GANs)

Rizvi, S. K. J., Azad, M. A., & Fraz, M. M. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09543-4 · Cited by 37

Classification of the emotional stress and physical stress using signal magnification and canonical correlation analysis

Hong, K., Liu, G., Chen, W., & Hong, S. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.12.013 · Cited by 34

Vehicle Detection Based on Drone Images with the Improved Faster R-CNN

Wang, L., Liao, J., & Xu, C. (2019) · Proceedings of the 2019 11th International Conference on Machine Learning and Computing · doi:10.1145/3318299.3318383 · Cited by 31

Combining Automatic Speaker Verification and Prosody Analysis for Synthetic Speech Detection

Attorresi, L., Salvi, D., Borrelli, C., Bestagini, P., & Tubaro, S. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-37742-6_21 · Cited by 30

A single-shot multi-level feature reused neural network for object detection

Wei, L., Cui, W., Hu, Z., Sun, H., & Hou, S. (2021) · The Visual Computer · doi:10.1007/s00371-019-01787-3 · Cited by 29

psfgan: a generative adversarial network system for separating quasar point sources and host galaxy light

Stark, D., Launet, B., Schawinski, K., Zhang, C., Koss, M., Turp, M. D., Sartori, L. F., Zhang, H., Chen, Y., & Weigel, A. K. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty764 · Cited by 24

Robust Vision-Based Cheat Detection in Competitive Gaming

Jonnalagadda, A., Frosio, I., Schneider, S., McGuire, M., & Kim, J. (2021) · Proceedings of the ACM on Computer Graphics and Interactive Techniques · doi:10.1145/3451259 · Cited by 23

Long-term variable Q transform: A novel time-frequency transform algorithm for synthetic speech detection

Li, J., Wang, H., He, P., Abdullahi, S. M., & Li, B. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2021.103256 · Cited by 20

Forest point processes for the automatic extraction of networks in raster data

Schmidt, A., Lafarge, F., Brenner, C., Rottensteiner, F., & Heipke, C. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.01.012 · Cited by 21

Untitled work

(2020) · doi:10.1145/3394486.3406479 · Cited by 20

RAMS: Remote and automatic mammogram screening

Cogan, T., Cogan, M., & Tamil, L. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.01.024 · Cited by 18

A thermographic visual inspection system for crack detection in metal parts exploiting a robotic workcell

Ghidoni, S., Antonello, M., Nanni, L., & Menegatti, E. (2015) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2015.07.020 · Cited by 18

Pedestrian tracking by learning deep features

Huang, H., Xu, Y., Huang, Y., Yang, Q., & Zhou, Z. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.11.001 · Cited by 15

A deep learning-based interactive medical image segmentation framework with sequential memory

Mikhailov, I., Chauveau, B., Bourdel, N., & Bartoli, A. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108038 · Cited by 15

GF-KCF: Aerial infrared target tracking algorithm based on kernel correlation filters under complex interference environment

Yang, X., Li, S., Yu, J., Zhang, K., Yang, J., & Yan, J. (2021) · Infrared Physics & Technology · doi:10.1016/j.infrared.2021.103958 · Cited by 14

Method for detection of unsafe actions in power field based on edge computing architecture

Yin, Y., Lin, J., Sun, N., Zhu, Q., Zhang, S., Zhang, Y., & Liu, M. (2021) · Journal of Cloud Computing · doi:10.1186/s13677-021-00234-w · Cited by 14

Artificial Intelligence in Medicine: Validation and Study Design

Oakden-Rayner, L., & Palmer, L. J. (2019) · Artificial Intelligence in Medical Imaging · doi:10.1007/978-3-319-94878-2_8 · Cited by 14

Towards Human-Like Emergent Communication via Utility, Informativeness, and Complexity

Tucker, M., Shah, J., Levy, R., & Zaslavsky, N. (2025) · Open Mind · doi:10.1162/opmi_a_00188 · Cited by 2

A hand-arm teleoperation system for robotic dexterous manipulation

Li, S., Li, Q., & Zhang, J. (2022) · Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation · doi:10.1016/b978-0-32-390445-2.00021-0 · Cited by 2

Protein pKa prediction with machine learning

Cai, Z., Luo, F., Wang, Y., Li, E., & Huang, Y. (2021) · doi:10.33774/chemrxiv-2021-7gk5l-v2 · Cited by 2