Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423

DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., & Bethge, M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0209-y · Cited by 5,187

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

Zhou, Y., & Tuzel, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00472 · Cited by 4,813

Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498

Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

Xie, T., & Grossman, J. C. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.145301 · Cited by 2,828

All-optical machine learning using diffractive deep neural networks

Lin, X., Rivenson, Y., Yardimci, N. T., Veli, M., Luo, Y., Jarrahi, M., & Ozcan, A. (2018) · Science · doi:10.1126/science.aat8084 · Cited by 2,568

ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope

Ray, P. P. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.04.003 · Cited by 2,463

A Database and Evaluation Methodology for Optical Flow

Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205

Deep visual domain adaptation: A survey

Wang, M., & Deng, W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.083 · Cited by 2,209

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

SchNet – A deep learning architecture for molecules and materials

Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019779 · Cited by 2,217

Deep Interest Network for Click-Through Rate Prediction

Zhou, G., Zhu, X., Song, C., Fan, Y., Zhu, H., Ma, X., Yan, Y., Jin, J., Li, H., & Gai, K. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3219823 · Cited by 2,128

Attention gated networks: learning to leverage salient regions in medical images

Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.012 · Cited by 2,014

YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness

Varghese, R., & M., S. (2024) · 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) · doi:10.1109/adics58448.2024.10533619 · Cited by 2,007

Matterport3D: Learning from RGB-D Data in Indoor Environments

Chang, A., Dai, A., Funkhouser, T., Halber, M., Niebner, M., Savva, M., Song, S., Zeng, A., & Zhang, Y. (2017) · 2017 International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00081 · Cited by 1,858

FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance

Cummins, M., & Newman, P. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364908090961 · Cited by 1,472

A Comprehensive Overview of Large Language Models

Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., & Mian, A. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3744746 · Cited by 1,257

Activation functions in deep learning: a comprehensive survey and benchmark

Dubey, S. R., Singh, S. K., & Chaudhuri, B. B. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.111 · Cited by 1,259

A survey on deep learning for big data

Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.10.006 · Cited by 1,226

Deep Learning on Image Denoising: An overview

Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., & Lin, C.-W. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.07.025 · Cited by 1,091

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J. N., Wu, Z., & Ding, X. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101693 · Cited by 1,095

Multi-view learning overview: Recent progress and new challenges

Zhao, J., Xie, X., Xu, X., & Sun, S. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.007 · Cited by 1,002

Pre-trained models: Past, present and future

Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., et al. (2021) · AI Open · doi:10.1016/j.aiopen.2021.08.002 · Cited by 994

A Comprehensive Survey of Deep Learning for Image Captioning

Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019) · ACM Computing Surveys · doi:10.1145/3295748 · Cited by 879

Computational psychiatry

Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.11.018 · Cited by 809

A deep learning model integrating FCNNs and CRFs for brain tumor segmentation

Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., & Fan, Y. (2018) · Medical Image Analysis · doi:10.1016/j.media.2017.10.002 · Cited by 813

Human motion tracking for rehabilitation-A survey

Zhou, H., & Hu, H. (2008) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2007.09.001 · Cited by 813

Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities

Saeed, W., & Omlin, C. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110273 · Cited by 783

Deep neural network models for computational histopathology: A survey

Srinidhi, C. L., Ciga, O., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101813 · Cited by 749

Deep learning in computer vision: A critical review of emerging techniques and application scenarios

Chai, J., Zeng, H., Li, A., & Ngai, E. W. T. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100134 · Cited by 754

Sensor-based and vision-based human activity recognition: A comprehensive survey

Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107561 · Cited by 730

A survey of appearance models in visual object tracking

Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., & Hengel, A. V. D. (2013) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2508037.2508039 · Cited by 705

Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification

Zhu, Y., & Zabaras, N. (2018) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.04.018 · Cited by 704

Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis

Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1919012117 · Cited by 685

PadChest: A large chest x-ray image dataset with multi-label annotated reports

Bustos, A., Pertusa, A., Salinas, J.-M., & de la Iglesia-Vayá, M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101797 · Cited by 691

Deep learning in video multi-object tracking: A survey

Ciaparrone, G., Luque Sánchez, F., Tabik, S., Troiano, L., Tagliaferri, R., & Herrera, F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.023 · Cited by 665

Pan-cancer integrative histology-genomic analysis via multimodal deep learning

Chen, R. J., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Noor, Z., Shaban, M., Shady, M., Williams, M., Joo, B., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.07.004 · Cited by 660

Loss odyssey in medical image segmentation

Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102035 · Cited by 662

What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research

Langer, M., Oster, D., Speith, T., Hermanns, H., Kästner, L., Schmidt, E., Sesing, A., & Baum, K. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103473 · Cited by 652

Simultaneous Localization, Mapping and Moving Object Tracking

Wang, C.-C., Thorpe, C., Thrun, S., Hebert, M., & Durrant-Whyte, H. (2007) · The International Journal of Robotics Research · doi:10.1177/0278364907081229 · Cited by 596

Notions of explainability and evaluation approaches for explainable artificial intelligence

Vilone, G., & Longo, L. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.009 · Cited by 598

Self-supervised learning for medical image analysis using image context restoration

Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101539 · Cited by 575

SummEval: Re-evaluating Summarization Evaluation

Fabbri, A. R., Kryściński, W., McCann, B., Xiong, C., Socher, R., & Radev, D. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00373 · Cited by 587

Video-based emotion recognition using CNN-RNN and C3D hybrid networks

Fan, Y., Lu, X., Li, D., & Liu, Y. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2997632 · Cited by 556

Weighted boxes fusion: Ensembling boxes from different object detection models

Solovyev, R., Wang, W., & Gabruseva, T. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104117 · Cited by 543

Using deep learning and Google street view to estimate the demographic makeup of neighborhoods across the United States.

Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E. L., & Fei-Fei, L. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1700035114 · Cited by 518

Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks

Yao, J., Zhu, X., Jonnagaddala, J., Hawkins, N., & Huang, J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101789 · Cited by 522

Deep convolutional neural networks for Raman spectrum recognition: a unified solution

Liu, J., Osadchy, M., Ashton, L., Foster, M., Solomon, C. J., & Gibson, S. J. (2017) · The Analyst · doi:10.1039/c7an01371j · Cited by 507

Deep neural networks for bot detection

Kudugunta, S., & Ferrara, E. (2018) · Information Sciences · doi:10.1016/j.ins.2018.08.019 · Cited by 506

Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments

Wu, D., Lv, S., Jiang, M., & Song, H. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105742 · Cited by 496

Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results

Li, X., Gu, Y., Dvornek, N., Staib, L. H., Ventola, P., & Duncan, J. S. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101765 · Cited by 480

Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces

Dalca, A. V., Balakrishnan, G., Guttag, J., & Sabuncu, M. R. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.07.006 · Cited by 467

Embracing Change: Continual Learning in Deep Neural Networks

Hadsell, R., Rao, D., Rusu, A. A., & Pascanu, R. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.09.004 · Cited by 467

Supervised Machine Learning for Population Genetics: A New Paradigm

Schrider, D. R., & Kern, A. D. (2018) · Trends in Genetics · doi:10.1016/j.tig.2017.12.005 · Cited by 453

Machine Learning with Membership Privacy using Adversarial Regularization

Nasr, M., Shokri, R., & Houmansadr, A. (2018) · Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3243734.3243855 · Cited by 458

Security and Privacy Challenges of Large Language Models: A Survey

Das, B. C., Amini, M. H., & Wu, Y. (2025) · ACM Computing Surveys · doi:10.1145/3712001 · Cited by 445

Medical image retrieval using deep convolutional neural network

Qayyum, A., Anwar, S. M., Awais, M., & Majid, M. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.05.025 · Cited by 433

Untitled work

(2022) · doi:10.1145/3503161.3548541 · Cited by 428

ResNet and its application to medical image processing: Research progress and challenges

Xu, W., Fu, Y.-L., & Zhu, D. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107660 · Cited by 416

Deep neural networks are superior to dermatologists in melanoma image classification

Brinker, T. J., Hekler, A., Enk, A. H., Berking, C., Haferkamp, S., Hauschild, A., Weichenthal, M., Klode, J., Schadendorf, D., Holland-Letz, T., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.05.023 · Cited by 415

Prevalence of neural collapse during the terminal phase of deep learning training

Papyan, V., Han, X. Y., & Donoho, D. L. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2015509117 · Cited by 403

A cloud detection algorithm for satellite imagery based on deep learning

Jeppesen, J. H., Jacobsen, R. H., Inceoglu, F., & Toftegaard, T. S. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.03.039 · Cited by 402

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

Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning

Zhou, L.-Q., Wu, X.-L., Huang, S.-Y., Wu, G.-G., Ye, H.-R., Wei, Q., Bao, L.-Y., Deng, Y.-B., Li, X.-R., Cui, X.-W., et al. (2020) · Radiology · doi:10.1148/radiol.2019190372 · Cited by 384

A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop

Langlotz, C. P., Allen, B., Erickson, B. J., Kalpathy-Cramer, J., Bigelow, K., Cook, T. S., Flanders, A. E., Lungren, M. P., Mendelson, D. S., Rudie, J. D., et al. (2019) · Radiology · doi:10.1148/radiol.2019190613 · Cited by 368

The present and future of deep learning in radiology

Saba, L., Biswas, M., Kuppili, V., Cuadrado Godia, E., Suri, H. S., Edla, D. R., Omerzu, T., Laird, J. R., Khanna, N. N., Mavrogeni, S., et al. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.02.038 · Cited by 353

Computer vision for behaviour-based safety in construction: A review and future directions

Fang, W., Love, P. E. D., Luo, H., & Ding, L. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2019.100980 · Cited by 346

Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images

Khosravi, P., Kazemi, E., Imielinski, M., Elemento, O., & Hajirasouliha, I. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2017.12.026 · Cited by 334

A survey on deep learning-based fine-grained object classification and semantic segmentation

Zhao, B., Feng, J., Wu, X., & Yan, S. (2017) · International Journal of Automation and Computing · doi:10.1007/s11633-017-1053-3 · Cited by 339

Detecting mammals in UAV images: best practices to address a substantially imbalanced dataset with deep learning

Kellenberger, B., Marcos, D., & Tuia, D. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.06.028 · Cited by 335

Multimodal sentiment analysis with word-level fusion and reinforcement learning

Chen, M., Wang, S., Liang, P. P., Baltrušaitis, T., Zadeh, A., & Morency, L.-P. (2017) · Proceedings of the 19th ACM International Conference on Multimodal Interaction · doi:10.1145/3136755.3136801 · Cited by 334

Outlier Detection: Methods, Models, and Classification

Boukerche, A., Zheng, L., & Alfandi, O. (2021) · ACM Computing Surveys · doi:10.1145/3381028 · Cited by 334

Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks

Wurm, M., Stark, T., Zhu, X. X., Weigand, M., & Taubenböck, H. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.02.006 · Cited by 332

Deep learning in robotics: A review of recent research

Pierson, H. A., & Gashler, M. S. (2017) · Advanced Robotics · doi:10.1080/01691864.2017.1365009 · Cited by 331

A comprehensive review on deep learning-based methods for video anomaly detection

Nayak, R., Pati, U. C., & Das, S. K. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2020.104078 · Cited by 324

Utilizing graph machine learning within drug discovery and development

Gaudelet, T., Day, B., Jamasb, A. R., Soman, J., Regep, C., Liu, G., Hayter, J. B. R., Vickers, R., Roberts, C., Tang, J., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab159 · Cited by 313

RGCNN: Regularized Graph CNN for Point Cloud Segmentation

Te, G., Hu, W., Zheng, A., & Guo, Z. (2018) · Proceedings of the 26th ACM international conference on Multimedia · doi:10.1145/3240508.3240621 · Cited by 311

Birnet: Brain image registration using dual-supervised fully convolutional networks

Fan, J., Cao, X., Yap, P.-T., & Shen, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.03.006 · Cited by 297

Pre-trained convolutional neural networks as feature extractors for tuberculosis detection

Lopes, U. K., & Valiati, J. F. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.08.001 · Cited by 294

[DL] A Survey of FPGA-based Neural Network Inference Accelerators

Guo, K., Zeng, S., Yu, J., Wang, Y., & Yang, H. (2019) · ACM Transactions on Reconfigurable Technology and Systems · doi:10.1145/3289185 · Cited by 292

Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification

Böhle, M., Eitel, F., Weygandt, M., & Ritter, K. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00194 · Cited by 291

Global Canopy Height Regression and Uncertainty Estimation from GEDI LIDAR Waveforms with Deep Ensembles

Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112760 · Cited by 291

COVID-19 cough classification using machine learning and global smartphone recordings

Pahar, M., Klopper, M., Warren, R., & Niesler, T. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104572 · Cited by 290

A review of video surveillance systems

Elharrouss, O., Almaadeed, N., & Al-Maadeed, S. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103116 · Cited by 283

Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images

Hekler, A., Utikal, J. S., Enk, A. H., Solass, W., Schmitt, M., Klode, J., Schadendorf, D., Sondermann, W., Franklin, C., Bestvater, F., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.06.012 · Cited by 285

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan

Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101992 · Cited by 279

Differentiable learning of quantum circuit Born machines

Liu, J.-G., & Wang, L. (2018) · Physical Review A · doi:10.1103/physreva.98.062324 · Cited by 279

A deep-learning-based approach for fast and robust steel surface defects classification

Fu, G., Sun, P., Zhu, W., Yang, J., Cao, Y., Yang, M. Y., & Cao, Y. (2019) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.05.005 · Cited by 271

A review of Earth Artificial Intelligence

Sun, Z., Sandoval, L., Crystal-Ornelas, R., Mousavi, S. M., Wang, J., Lin, C., Cristea, N., Tong, D., Carande, W. H., Ma, X., et al. (2022) · Computers & Geosciences · doi:10.1016/j.cageo.2022.105034 · Cited by 271

Does human–AI collaboration lead to more creative art? Aesthetic evaluation of human-made and AI-generated haiku poetry

Hitsuwari, J., Ueda, Y., Yun, W., & Nomura, M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107502 · Cited by 267

Deep multimodal fusion for semantic image segmentation: A survey

Zhang, Y., Sidibé, D., Morel, O., & Mériaudeau, F. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2020.104042 · Cited by 261

Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders.

Han, S. S., Park, I., Eun Chang, S., Lim, W., Kim, M. S., Park, G. H., Chae, J. B., Huh, C. H., & Na, J.-I. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.01.019 · Cited by 248

Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

Ashmore, R., Calinescu, R., & Paterson, C. (2022) · ACM Computing Surveys · doi:10.1145/3453444 · Cited by 256

A quantitative uncertainty metric controls error in neural network-driven chemical discovery

Janet, J. P., Duan, C., Yang, T., Nandy, A., & Kulik, H. J. (2019) · Chemical Science · doi:10.1039/c9sc02298h · Cited by 243

Integrating Machine Learning with Human Knowledge

Deng, C., Ji, X., Rainey, C., Zhang, J., & Lu, W. (2020) · iScience · doi:10.1016/j.isci.2020.101656 · Cited by 243

Fast implementation of real-time fruit detection in apple orchards using deep learning

Kang, H., & Chen, C. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.105108 · Cited by 243

Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning

Jiang, Z., Dong, Z., Jiang, W., & Yang, Y. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106184 · Cited by 242

Surface defect classification of steels with a new semi-supervised learning method

Di, H., Ke, X., Peng, Z., & Dongdong, Z. (2019) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.01.011 · Cited by 241

Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database

Sirazitdinov, I., Kholiavchenko, M., Mustafaev, T., Yixuan, Y., Kuleev, R., & Ibragimov, B. (2019) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2019.08.004 · Cited by 240