Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

A guide to deep learning in healthcare

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312

Cellpose: a generalist algorithm for cellular segmentation

Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021) · Nature Methods · doi:10.1038/s41592-020-01018-x · Cited by 4,369

Adapting visual category models to new domains

Saenko, K., Kulis, B., Fritz, M., & Darrell, T. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15561-1_16 · Cited by 2,847

PCT: Point cloud transformer

Guo, M.-H., Cai, J.-X., Liu, Z.-N., Mu, T.-J., Martin, R. R., & Hu, S.-M. (2021) · Computational Visual Media · doi:10.1007/s41095-021-0229-5 · Cited by 2,100

Towards a general-purpose foundation model for computational pathology

Chen, R. J., Ding, T., Lu, M. Y., Williamson, D. F. K., Jaume, G., Song, A. H., Chen, B., Zhang, A., Shao, D., Shaban, M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02857-3 · Cited by 1,560

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A., & Viergever, M. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102470 · Cited by 1,328

A Survey of Deep Active Learning

Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., & Wang, X. (2022) · ACM Computing Surveys · doi:10.1145/3472291 · Cited by 1,169

Deep Learning: A Primer for Radiologists

Chartrand, G., Cheng, P. M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C. J., Kadoury, S., & Tang, A. (2017) · RadioGraphics · doi:10.1148/rg.2017170077 · Cited by 1,115

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

Visual simultaneous localization and mapping: a survey

Fuentes-Pacheco, J., Ruiz-Ascencio, J., & Rendón-Mancha, J. M. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-012-9365-8 · Cited by 883

Explainable Artificial Intelligence in education

Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100074 · Cited by 863

Measuring human perceptions of a large-scale urban region using machine learning

Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., & Ratti, C. (2018) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2018.08.020 · Cited by 810

Artificial intelligence for multimodal data integration in oncology

Lipkova, J., Chen, R. J., Chen, B., Lu, M. Y., Barbieri, M., Shao, D., Vaidya, A. J., Chen, C., Zhuang, L., Williamson, D. F. K., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.012 · Cited by 777

A survey and performance evaluation of deep learning methods for small object detection

Liu, Y., Sun, P., Wergeles, N., & Shang, Y. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114602 · Cited by 665

Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays

Brunese, L., Mercaldo, F., Reginelli, A., & Santone, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105608 · Cited by 623

Time-Series Anomaly Detection Service at Microsoft

Ren, H., Xu, B., Wang, Y., Yi, C., Huang, C., Kou, X., Xing, T., Yang, M., Tong, J., & Zhang, Q. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330680 · Cited by 623

A survey on active learning and human-in-the-loop deep learning for medical image analysis

Budd, S., Robinson, E. C., & Kainz, B. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102062 · Cited by 606

Deep visual tracking: Review and experimental comparison

Li, P., Wang, D., Wang, L., & Lu, H. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.11.007 · Cited by 517

Fairness in machine learning: A survey

Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646

A survey towards an integration of big data analytics to big insights for value-creation

Saggi, M. K., & Jain, S. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.01.010 · Cited by 494

Federated Learning and Differential Privacy for Medical Image Analysis

Adnan, M., Kalra, S., Cresswell, J. C., Taylor, G. W., & Tizhoosh, H. (2021) · doi:10.21203/rs.3.rs-1005694/v1 · Cited by 464

Interpretable and Explainable Machine Learning for Materials Science and Chemistry

Oviedo, F., Ferres, J. L., Buonassisi, T., & Butler, K. T. (2022) · Accounts of Materials Research · doi:10.1021/accountsmr.1c00244 · Cited by 435

MOLI: multi-omics late integration with deep neural networks for drug response prediction

Sharifi-Noghabi, H., Zolotareva, O., Collins, C. C., & Ester, M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz318 · Cited by 421

Deep learning and its application to LHC physics

Guest, D., Cranmer, K., & Whiteson, D. (2018) · Annual Review of Nuclear and Particle Science · doi:10.1146/annurev-nucl-101917-021019 · Cited by 417

Deep learning for decentralized parking lot occupancy detection

Amato, G., Carrara, F., Falchi, F., Gennaro, C., Meghini, C., & Vairo, C. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.10.055 · Cited by 397

Deep learning for classification of colorectal polyps on whole-slide images

Korbar, B., Olofson, A. M., Miraflor, A. P., Nicka, C. M., Suriawinata, M. A., Torresani, L., Suriawinata, A. A., & Hassanpour, S. (2017) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_34_17 · Cited by 349

RGB-D salient object detection: A survey

Zhou, T., Fan, D.-P., Cheng, M.-M., Shen, J., & Shao, L. (2021) · Computational Visual Media · doi:10.1007/s41095-020-0199-z · Cited by 331

Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks

Wolterink, J. M., Leiner, T., de Vos, B. D., van Hamersvelt, R. W., Viergever, M. A., & Išgum, I. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.04.004 · Cited by 321

ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes

Yeshwanth, C., Liu, Y.-C., Nießner, M., & Dai, A. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00008 · Cited by 316

A systematic survey of computer-aided diagnosis in medicine: Past and present developments

Yanase, J., & Triantaphyllou, E. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112821 · Cited by 303

Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection

Kanjo, E., Younis, E. M. G., & Ang, C. S. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.001 · Cited by 293

Hybrid quantum-classical convolutional neural networks

Liu, J., Lim, K. H., Wood, K. L., Huang, W., Guo, C., & Huang, H.-L. (2021) · Science China Physics, Mechanics & Astronomy · doi:10.1007/s11433-021-1734-3 · Cited by 289

Automated sleep scoring: A review of the latest approaches

Fiorillo, L., Puiatti, A., Papandrea, M., Ratti, P.-L., Favaro, P., Roth, C., Bargiotas, P., Bassetti, C. L., & Faraci, F. D. (2019) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2019.07.007 · Cited by 284

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Nazir, S., Dickson, D. M., & Akram, M. U. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106668 · Cited by 283

Human action recognition using fusion of multiview and deep features: an application to video surveillance

Khan, M. A., Javed, K., Khan, S. A., Saba, T., Habib, U., Khan, J. A., & Abbasi, A. A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08806-9 · Cited by 277

Neural Network Guided Interpolation for Mapping Canopy Height of China’s Forests by Integrating GEDI and ICESat-2 Data

Liu, X., Su, Y., Hu, T., Yang, Q., Liu, B., Deng, Y., Tang, H., Tang, Z., Fang, J., & Guo, Q. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112844 · Cited by 270

An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification

Shen, S., Han, S. X., Aberle, D. R., Bui, A. A., & Hsu, W. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.01.048 · Cited by 268

Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy

Wang, Z., She, Q., & Ward, T. E. (2022) · ACM Computing Surveys · doi:10.1145/3439723 · Cited by 267

Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms

Al-antari, M. A., Han, S.-M., & Kim, T.-S. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105584 · Cited by 264

A Survey on Deepfake Video Detection

Yu, P., Xia, Z., Fei, J., & Lu, Y. (2021) · IET Biometrics · doi:10.1049/bme2.12031 · Cited by 253

Camera-IMU-based localization: Observability analysis and consistency improvement

Hesch, J. A., Kottas, D. G., Bowman, S. L., & Roumeliotis, S. I. (2014) · The International Journal of Robotics Research · doi:10.1177/0278364913509675 · Cited by 245

Double image encryption algorithm based on neural network and chaos

Man, Z., Li, J., Di, X., Sheng, Y., & Liu, Z. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2021.111318 · Cited by 241

Experimental evidence of effective human–AI collaboration in medical decision-making

Reverberi, C., Rigon, T., Solari, A., Hassan, C., Cherubini, P., GI Genius CADx Study Group, Antonelli, G., Awadie, H., Bernhofer, S., Carballal, S., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-18751-2 · Cited by 281

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

Deep learning based on Batch Normalization for P300 signal detection

Liu, M., Wu, W., Gu, Z., Yu, Z., Qi, F., & Li, Y. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.08.039 · Cited by 230

Perceptual video quality assessment: a survey

Min, X., Duan, H., Sun, W., Zhu, Y., & Zhai, G. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4133-3 · Cited by 218

An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine

Özyurt, F., Sert, E., & Avcı, D. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2019.109433 · Cited by 210

Quantum Entanglement in Deep Learning Architectures

Levine, Y., Sharir, O., Cohen, N., & Shashua, A. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.065301 · Cited by 206

Designing interpretable ML system to enhance trust in healthcare: A systematic review to proposed responsible clinician-AI-collaboration framework

Nasarian, E., Alizadehsani, R., Acharya, U. R., & Tsui, K.-L. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102412 · Cited by 207

IoU-aware single-stage object detector for accurate localization

Wu, S., Li, X., & Wang, X. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.103911 · Cited by 202

REM sleep and neural nets

Crick, F., & Mitchison, G. (1995) · Behavioural Brain Research · doi:10.1016/0166-4328(95)00006-f · Cited by 195

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

Animal biometrics: quantifying and detecting phenotypic appearance

Kühl, H. S., & Burghardt, T. (2013) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2013.02.013 · Cited by 189

Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine-and deep-learning methods

Herriott, C., & Spear, A. D. (2020) · Computational Materials Science · doi:10.1016/j.commatsci.2020.109599 · Cited by 187

Deep Adversarial Learning for Multi-Modality Missing Data Completion

Cai, L., Wang, Z., Gao, H., Shen, D., & Ji, S. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3219963 · Cited by 180

A survey of Deep Neural Network watermarking techniques

Li, Y., Wang, H., & Barni, M. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.051 · Cited by 177

Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier

Abraham, B., & Nair, M. S. (2020) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2020.08.005 · Cited by 169

COVID-19 image classification using deep learning: Advances, challenges and opportunities

Aggarwal, P., Mishra, N. K., Fatimah, B., Singh, P., Gupta, A., & Joshi, S. D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105350 · Cited by 168

OCRBench: on the hidden mystery of OCR in large multimodal models

Liu, Y., Li, Z., Huang, M., Yang, B., Yu, W., Li, C., Yin, X.-C., Liu, C.-L., Jin, L., & Bai, X. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4235-6 · Cited by 167

AI-assisted knowledge assessment techniques for adaptive learning environments

Minn, S. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100050 · Cited by 166

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

A review on object detection in unmanned aerial vehicle surveillance

Ramachandran, A., & Sangaiah, A. K. (2021) · International Journal of Cognitive Computing in Engineering · doi:10.1016/j.ijcce.2021.11.005 · Cited by 165

Loss of plasticity in deep continual learning

Dohare, S., Hernandez-Garcia, J. F., Lan, Q., Rahman, P., Mahmood, A. R., & Sutton, R. S. (2024) · Nature · doi:10.1038/s41586-024-07711-7 · Cited by 172

A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic

Sufian, A., Ghosh, A., Sadiq, A. S., & Smarandache, F. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101830 · Cited by 156

A blockchain-based fog computing framework for activity recognition as an application to e-healthcare services

Islam, N., Faheem, Y., Din, I. U., Talha, M., Guizani, M., & Khalil, M. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.05.059 · Cited by 154

Weaponized AI for cyber attacks

Yamin, M. M., Ullah, M., Ullah, H., & Katt, B. (2021) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2020.102722 · Cited by 154

Traffic flow estimation with data from a video surveillance camera

Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., & Minbaleev, A. (2019) · Journal Of Big Data · doi:10.1186/s40537-019-0234-z · Cited by 149

Addressing Artificial Intelligence Bias in Retinal Diagnostics

Burlina, P., Joshi, N., Paul, W., Pacheco, K. D., & Bressler, N. M. (2021) · Translational Vision Science & Technology · doi:10.1167/tvst.10.2.13 · Cited by 138

A Hybrid Deep Learning Model for Predicting Molecular Subtypes of Human Breast Cancer Using Multimodal Data

Liu, T., Huang, J., Liao, T., Pu, R., Liu, S., & Peng, Y. (2022) · IRBM · doi:10.1016/j.irbm.2020.12.002 · Cited by 137

Neural probabilistic logic programming in DeepProbLog

Manhaeve, R., Dumančić, S., Kimmig, A., Demeester, T., & De Raedt, L. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103504 · Cited by 131

Multi-task learning for quality assessment of fetal head ultrasound images

Lin, Z., Li, S., Ni, D., Liao, Y., Wen, H., Du, J., Chen, S., Wang, T., & Lei, B. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101548 · Cited by 129

Fairness in Machine Learning: A Survey

Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646

Effectively Detecting Operational Anomalies In Large-Scale IoT Data Infrastructures By Using A GAN-Based Predictive Model

Chen, P., Liu, H., Xin, R., Carval, T., Zhao, J., Xia, Y., & Zhao, Z. (2022) · The Computer Journal · doi:10.1093/comjnl/bxac085 · Cited by 128

Toward fairness in AI for people with disabilities SBG@a research roadmap

Guo, A., Kamar, E., Vaughan, J. W., Wallach, H., & Morris, M. R. (2020) · ACM SIGACCESS Accessibility and Computing · doi:10.1145/3386296.3386298 · Cited by 128

Overcoming Catastrophic Forgetting in Graph Neural Networks

Liu, H., Yang, Y., & Wang, X. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17049 · Cited by 127

Modeling of deformable objects for robotic manipulation: A tutorial and review

Arriola-Rios, V. E., Guler, P., Ficuciello, F., Kragic, D., Siciliano, B., & Wyatt, J. L. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00082 · Cited by 126

Computer vision in automated parking systems: Design, implementation and challenges

Heimberger, M., Horgan, J., Hughes, C., McDonald, J., & Yogamani, S. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.07.002 · Cited by 126

Advances in intelligent and autonomous navigation systems for small UAS

Bijjahalli, S., Sabatini, R., & Gardi, A. (2020) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2020.100617 · Cited by 123

Modality-correlation-aware sparse representation for RGB-infrared object tracking

Lan, X., Ye, M., Zhang, S., Zhou, H., & Yuen, P. C. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.10.002 · Cited by 121

ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis

Chen, Y., Peng, G., Xie, C., Zhang, W., Li, C., & Liu, S. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.03.014 · Cited by 121

Optical Flow based CNN for detection of unlearnt deepfake manipulations

Caldelli, R., Galteri, L., Amerini, I., & Del Bimbo, A. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.03.005 · Cited by 115

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

HoloNet: towards robust emotion recognition in the wild

Yao, A., Cai, D., Hu, P., Wang, S., Sha, L., & Chen, Y. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2997639 · Cited by 115

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

HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising

Shabani, M. A., Hosseini, S., & Furukawa, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00529 · Cited by 124

Demystifying Brain Tumor Segmentation Networks: Interpretability and Uncertainty Analysis

Natekar, P., Kori, A., & Krishnamurthi, G. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00006 · Cited by 103

Towards occupant activity driven smart buildings via WiFi-enabled IoT devices and deep learning

Zou, H., Zhou, Y., Yang, J., & Spanos, C. J. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.08.010 · Cited by 100

Multimodal data fusion for systems improvement: A review

Gaw, N., Yousefi, S., & Gahrooei, M. R. (2022) · IISE Transactions · doi:10.1080/24725854.2021.1987593 · Cited by 101

CRISPR‐Net: A Recurrent Convolutional Network Quantifies CRISPR Off‐Target Activities with Mismatches and Indels

Lin, J., Zhang, Z., Zhang, S., Chen, J., & Wong, K. (2020) · Advanced Science · doi:10.1002/advs.201903562 · Cited by 101

Incomplete multi-modal representation learning for Alzheimer’s disease diagnosis

Liu, Y., Fan, L., Zhang, C., Zhou, T., Xiao, Z., Geng, L., & Shen, D. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101953 · Cited by 102

NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster

Lobato-Rios, V., Ramalingasetty, S. T., Özdil, P. G., Arreguit, J., Ijspeert, A. J., & Ramdya, P. (2022) · Nature Methods · doi:10.1038/s41592-022-01466-7 · Cited by 88

Densely nested top-down flows for salient object detection

Fang, C., Tian, H., Zhang, D., Zhang, Q., Han, J., & Han, J. (2022) · Science China Information Sciences · doi:10.1007/s11432-021-3384-y · Cited by 98

A novel Enhanced Collaborative Autoencoder with knowledge distillation for top-N recommender systems

Pan, Y., He, F., & Yu, H. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.12.025 · Cited by 97

Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment

Jiménez-Luna, J., Skalic, M., Weskamp, N., & Schneider, G. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01344 · Cited by 96

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

Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs

Millidge, B., Tschantz, A., & Buckley, C. L. (2022) · Neural Computation · doi:10.1162/neco_a_01497 · Cited by 95

Adversarial attack and defense technologies in natural language processing: A survey

Qiu, S., Liu, Q., Zhou, S., & Huang, W. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.020 · Cited by 94

Deep balanced discrete hashing for image retrieval

Zheng, X., Zhang, Y., & Lu, X. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.037 · Cited by 83

Convolutional neural network based automatic screening tool for cardiovascular diseases using different intervals of ECG signals

Dai, H., Hwang, H.-G., & Tseng, V. S. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106035 · Cited by 81

Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network

(2020) · doi:10.1016/j.neurobiolaging.2020.12.005 · Cited by 78