2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Saxena, D., & Cao, J. (2022) · ACM Computing Surveys · doi:10.1145/3446374 · Cited by 689

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

Skeleton-based action recognition via spatial and temporal transformer networks

Plizzari, C., Cannici, M., & Matteucci, M. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103219 · Cited by 409

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

A review of deep learning techniques for speech processing

Mehrish, A., Majumder, N., Bharadwaj, R., Mihalcea, R., & Poria, S. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101869 · Cited by 389

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

Deep metric learning for few-shot image classification: A Review of recent developments

Li, X., Yang, X., Ma, Z., & Xue, J.-H. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109381 · Cited by 243

A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives

Mathis, A., Schneider, S., Lauer, J., & Mathis, M. W. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.017 · Cited by 231

A survey on few-shot class-incremental learning

Tian, S., Li, L., Li, W., Ran, H., Ning, X., & Tiwari, P. (2024) · Neural Networks · doi:10.1016/j.neunet.2023.10.039 · Cited by 220

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

The phenotypic landscape of essential human genes

Funk, L., Su, K.-C., Ly, J., Feldman, D., Singh, A., Moodie, B., Blainey, P. C., & Cheeseman, I. M. (2022) · Cell · doi:10.1016/j.cell.2022.10.017 · Cited by 160

Eff2Net: An efficient channel attention-based convolutional neural network for skin disease classification

Karthik, R., Vaichole, T. S., Kulkarni, S. K., Yadav, O., & Khan, F. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103406 · Cited by 152

Toward immersive communications in 6G

Shen, X. (., Gao, J., Li, M., Zhou, C., Hu, S., He, M., & Zhuang, W. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.1068478 · Cited by 124

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

Deep learning and the global workspace theory

VanRullen, R., & Kanai, R. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.04.005 · Cited by 128

Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications

Schouten, D., Nicoletti, G., Dille, B., Chia, C., Vendittelli, P., Schuurmans, M., Litjens, G., & Khalili, N. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103621 · Cited by 162

Use of artificial intelligence and deep learning in fetal ultrasound imaging

Ramirez Zegarra, R., & Ghi, T. (2023) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.26130 · Cited by 102

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

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

EL-YOLO: An efficient and lightweight low-altitude aerial objects detector for onboard applications

Xue, C., Xia, Y., Wu, M., Chen, Z., Cheng, F., & Yun, L. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124848 · Cited by 82

Live-cell imaging powered by computation

Shroff, H., Testa, I., Jug, F., & Manley, S. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00702-6 · Cited by 75

Deepfake forensics analysis: An explainable hierarchical ensemble of weakly supervised models

Silva, S. H., Bethany, M., Votto, A. M., Scarff, I. H., Beebe, N., & Najafirad, P. (2022) · Forensic Science International: Synergy · doi:10.1016/j.fsisyn.2022.100217 · Cited by 66

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

Learning to denoise astronomical images with U-nets

Vojtekova, A., Lieu, M., Valtchanov, I., Altieri, B., Old, L., Chen, Q., & Hroch, F. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa3567 · Cited by 63

AI Agents and Agentic AI–navigating a plethora of concepts for future manufacturing

Ren, Y., Liu, Y., Ji, T., & Xu, X. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.08.017 · Cited by 66

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

Multimodal Image Fusion Offers Better Spatial Resolution for Mass Spectrometry Imaging

Guo, L., Zhu, J., Wang, K., Cheng, K.-K., Xu, J., Dong, L., Xu, X., Chen, C., Shah, M., Peng, Z., et al. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c02002 · Cited by 36

State-of-Health prediction of lithium-ion batteries based on a low dimensional Gaussian Process Regression

Pohlmann, S., Mashayekh, A., Stroebl, F., Karnehm, D., Kuder, M., Neve, A., & Weyh, T. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111649 · Cited by 32

Hybrid deep learning model using SPCAGAN augmentation for insider threat analysis

R.G., G., Sajjanhar, A., & Xiang, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123533 · Cited by 28

Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging

Turgut, Ö., Müller, P., Hager, P., Shit, S., Starck, S., Menten, M. J., Martens, E., & Rueckert, D. (2025) · Medical Image Analysis · doi:10.1016/j.media.2024.103451 · Cited by 22

A comprehensive survey and taxonomy on privacy-preserving deep learning

Tran, A.-T., Luong, T.-D., & Huynh, V.-N. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127345 · Cited by 21

Explainable AI in human motion: A comprehensive approach to analysis, modeling, and generation

Olivas-Padilla, B. E., Manitsaris, S., & Glushkova, A. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110418 · Cited by 19

ASL champ!: a virtual reality game with deep-learning driven sign recognition

Alam, M. S., Lamberton, J., Wang, J., Leannah, C., Miller, S., Palagano, J., Bastion, M. D., Smith, H. L., Malzkuhn, M., & Quandt, L. C. (2024) · Computers & Education: X Reality · doi:10.1016/j.cexr.2024.100059 · Cited by 19

Quantitative analysis of automatic voice disorder detection studies for hybrid feature and classifier selection

Lee, J. B., & Lee, H. G. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106014 · 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

Utilizing Data Imbalance to Enhance Compound–Protein Interaction Prediction Models

(2025) · doi:10.1002/aisy.202400985 · Cited by 3