Learning Deep Architectures for AI

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001

Internet of Things (IoT): A vision, architectural elements, and future directions

Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013) · Future Generation Computer Systems · doi:10.1016/j.future.2013.01.010 · Cited by 12,293

Machine learning: Trends, perspectives, and prospects

Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542

Context Encoders: Feature Learning by Inpainting

Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691

Curriculum learning

Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366

A random forest guided tour

Biau, G., & Scornet, E. (2016) · Test · doi:10.1007/s11749-016-0481-7 · Cited by 4,149

The Limitations of Deep Learning in Adversarial Settings

Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2016) · 2016 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp.2016.36 · Cited by 3,993

Machine Learning in Medicine

Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466

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

Deep learning for computer vision: A brief review

Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/7068349 · Cited by 3,403

A few useful things to know about machine learning

Domingos, P. (2012) · Communications of the ACM · doi:10.1145/2347736.2347755 · Cited by 3,326

Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

Nguyen, A., Yosinski, J., & Clune, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298640 · Cited by 3,258

A survey of deep neural network architectures and their applications

Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239

A survey of the recent architectures of deep convolutional neural networks

Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09825-6 · Cited by 3,137

Deep learning for healthcare: review, opportunities and challenges

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076

Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

Philip Chen, C. L., & Zhang, C.-Y. (2014) · Information Sciences · doi:10.1016/j.ins.2014.01.015 · Cited by 3,047

Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition

Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2134090 · Cited by 3,011

Traffic Flow Prediction With Big Data: A Deep Learning Approach

Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F.-Y. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2345663 · Cited by 3,000

Applications of machine learning to machine fault diagnosis: A review and roadmap

Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997

Structural Deep Network Embedding

Wang, D., Cui, P., & Zhu, W. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939753 · Cited by 2,845

Deep learning for visual understanding: A review

Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.09.116 · Cited by 2,504

Privacy-preserving deep learning

Shokri, R., & Shmatikov, V. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813687 · Cited by 2,356

Deep visual domain adaptation: A survey

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

Artificial intelligence for fault diagnosis of rotating machinery: A review

Liu, R., Yang, B., Zio, E., & Chen, X. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.02.016 · Cited by 2,188

Deep Metric Learning Using Triplet Network

Hoffer, E., & Ailon, N. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24261-3_7 · Cited by 2,134

Stochastic blockmodels and community structure in networks

Karrer, B., & Newman, M. E. J. (2011) · Physical Review E · doi:10.1103/physreve.83.016107 · Cited by 2,025

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

Akhtar, N., & Mian, A. (2018) · IEEE Access · doi:10.1109/access.2018.2807385 · Cited by 2,021

Practical Recommendations for Gradient-Based Training of Deep Architectures

Bengio, Y. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_26 · Cited by 2,259

How does the brain solve visual object recognition?

DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012) · Neuron · doi:10.1016/j.neuron.2012.01.010 · Cited by 1,790

Causability and explainability of artificial intelligence in medicine

Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1312 · Cited by 1,885

Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning

Sharma, P., Ding, N., Goodman, S., & Soricut, R. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1238 · Cited by 1,879

The rise of deep learning in drug discovery

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770

Using goal-driven deep learning models to understand sensory cortex

Yamins, D. L. K., & DiCarlo, J. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4244 · Cited by 1,621

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2844341 · Cited by 1,471

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392

Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning

Hitaj, B., Ateniese, G., & Perez-Cruz, F. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134012 · Cited by 1,377

Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science

Agrawal, A., & Choudhary, A. (2016) · APL Materials · doi:10.1063/1.4946894 · Cited by 1,385

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

Big data deep learning: Challenges and perspectives

Chen, X.-W., & Lin, X. (2014) · IEEE Access · doi:10.1109/access.2014.2325029 · Cited by 1,255

Deep supervised, but not unsupervised, models may explain IT cortical representation

Khaligh-Razavi, S.-M., & Kriegeskorte, N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003915 · Cited by 1,204

High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

Erfani, S. M., Rajasegarar, S., Karunasekera, S., & Leckie, C. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.03.028 · Cited by 1,162

Deep packet: a novel approach for encrypted traffic classification using deep learning

Lotfollahi, M., Jafari Siavoshani, M., Shirali Hossein Zade, R., & Saberian, M. (2020) · Soft Computing · doi:10.1007/s00500-019-04030-2 · Cited by 1,190

Deep neural networks: a new framework for modeling biological vision and brain information processing

Kriegeskorte, N. (2015) · Annual Review of Vision Science · doi:10.1146/annurev-vision-082114-035447 · Cited by 1,093

Majorana zero modes and topological quantum computation

Sarma, S. D., Freedman, M., & Nayak, C. (2015) · npj Quantum Information · doi:10.1038/npjqi.2015.1 · Cited by 1,180

Data Center Energy Consumption Modeling: A Survey

Dayarathna, M., Wen, Y., & Fan, R. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2481183 · Cited by 1,165

Deep learning-based multi-omics integration robustly predicts survival in liver cancer

Chaudhary, K., Poirion, O. B., Lu, L., & Garmire, L. X. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-0853 · Cited by 1,091

Imitation Learning: A Survey of Learning Methods

Hussein, A., Gaber, M. M., Elyan, E., & Jayne, C. (2018) · ACM Computing Surveys · doi:10.1145/3054912 · Cited by 1,060

Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis

Suk, H.-I., Lee, S.-W., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.077 · Cited by 968

Large scale deep learning for computer aided detection of mammographic lesions

Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C. I., Mann, R., den Heeten, A., & Karssemeijer, N. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.07.007 · Cited by 965

Using recurrent neural network models for early detection of heart failure onset

Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw112 · Cited by 928

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

Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors

Zeng, M., Nguyen, L. T., Yu, B., Mengshoel, O. J., Zhu, J., Wu, P., & Zhang, J. (2014) · Proceedings of the 6th International Conference on Mobile Computing, Applications and Services · doi:10.4108/icst.mobicase.2014.257786 · Cited by 876

Machine learning in wireless sensor networks: Algorithms, strategies, and applications

Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2320099 · Cited by 965

Deep Direct Reinforcement Learning for Financial Signal Representation and Trading

Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2522401 · Cited by 822

A survey on deep learning for multimodal data fusion

Gao, J., Li, P., Chen, Z., & Zhang, J. (2020) · Neural Computation · doi:10.1162/neco_a_01273 · Cited by 821

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810

Applications of deep learning and reinforcement learning to biological data

Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2790388 · Cited by 765

Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data

Jo, T., Nho, K., & Saykin, A. J. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00220 · Cited by 724

Deep Learning in Drug Discovery

Gawehn, E., Hiss, J. A., & Schneider, G. (2016) · Molecular Informatics · doi:10.1002/minf.201501008 · Cited by 723

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

Speech Emotion Recognition Using Deep Learning Techniques: A Review

Khalil, R. A., Jones, E., Babar, M. I., Jan, T., Zafar, M. H., & Alhussain, T. (2019) · IEEE Access · doi:10.1109/access.2019.2936124 · Cited by 702

A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox

Jing, L., Zhao, M., Li, P., & Xu, X. (2017) · Measurement · doi:10.1016/j.measurement.2017.07.017 · Cited by 698

Artificial intelligence: a survey on evolution, models, applications and future trends

Lu, Y. (2019) · Journal of Management Analytics · doi:10.1080/23270012.2019.1570365 · Cited by 641

What do we need to build explainable AI systems for the medical domain?

Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017) · arXiv · doi:10.48550/arxiv.1712.09923 · Cited by 636

Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease

Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M. J., & ADNI (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2372011 · Cited by 637

Expert Gate: Lifelong Learning with a Network of Experts

Aljundi, R., Chakravarty, P., & Tuytelaars, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.753 · Cited by 619

Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers

Hohman, F., Kahng, M., Pienta, R., & Chau, D. H. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2843369 · Cited by 582

Deep hashing for compact binary codes learning

Liong, V. E., Lu, J., Wang, G., Moulin, P., & Zhou, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298862 · Cited by 545

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625

An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

Sayed, N., Huang, Y., Nguyen, K., Krejciova-Rajaniemi, Z., Grawe, A. P., Gao, T., Tibshirani, R., Hastie, T., Alpert, A., Cui, L., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00082-y · Cited by 572

Brain plasticity through the life span: Learning to learn and action video games

Bavelier, D., Green, C. S., Pouget, A., & Schrater, P. (2012) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-060909-152832 · Cited by 541

DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning

Angermueller, C., Lee, H. J., Reik, W., & Stegle, O. (2017) · Genome Biology · doi:10.1186/s13059-017-1189-z · Cited by 536

Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Mocanu, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M., & Liotta, A. (2018) · Nature Communications · doi:10.1038/s41467-018-04316-3 · Cited by 524

Transductive Multi-View Zero-Shot Learning

Fu, Y., Hospedales, T. M., Xiang, T., & Gong, S. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2408354 · Cited by 514

Multimodal intelligence: Representation learning, information fusion, and applications

Zhang, C., Yang, Z., He, X., & Deng, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2987728 · Cited by 511

Rico: A Mobile App Dataset for Building Data-Driven Design Applications

Deka, B., Huang, Z., Franzen, C., Hibschman, J., Afergan, D., Li, Y., Nichols, J., & Kumar, R. (2017) · Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3126594.3126651 · Cited by 482

Deep learning in microscopy image analysis: a survey

Xing, F., Xie, Y., Su, H., Liu, F., & Yang, L. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2766168 · Cited by 460

Minerva: enabling low-power, highly-accurate deep neural network accelerators

Reagen, B., Whatmough, P., Adolf, R., Rama, S., Lee, H., Lee, S. K., Hernández-Lobato, J. M., Wei, G.-Y., & Brooks, D. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001165 · Cited by 446

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

Multi-layer Representation Learning for Medical Concepts

Choi, E., Bahadori, M. T., Searles, E., Coffey, C., Thompson, M., Bost, J., Tejedor-Sojo, J., & Sun, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939823 · Cited by 469

Deep learning of the tissue-regulated splicing code

Leung, M. K. K., Xiong, H. Y., Lee, L. J., & Frey, B. J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu277 · Cited by 459

An up-to-date comparison of state-of-the-art classification algorithms

Zhang, C., Liu, C., Zhang, X., & Almpanidis, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.04.003 · Cited by 440

A tutorial on task-parameterized movement learning and retrieval

Calinon, S. (2016) · Intelligent Service Robotics · doi:10.1007/s11370-015-0187-9 · Cited by 422

Structural damage identification based on autoencoder neural networks and deep learning

Pathirage, C. S. N., Li, J., Li, L., Hao, H., Liu, W., & Ni, P. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.05.109 · Cited by 415

Deep learning for robust feature generation in audiovisual emotion recognition

Kim, Y., Lee, H., & Provost, E. M. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638346 · Cited by 401

Big Data: Deep Learning for financial sentiment analysis

Sohangir, S., Wang, D., Pomeranets, A., & Khoshgoftaar, T. M. (2018) · Journal of Big Data · doi:10.1186/s40537-017-0111-6 · Cited by 390

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

Boosting docking-based virtual screening with deep learning.

Pereira, J. C., Caffarena, E. R., & dos Santos, C. N. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00355 · Cited by 395

Why Does Deep and Cheap Learning Work So Well?

Lin, H. W., Tegmark, M., & Rolnick, D. (2017) · Journal of Statistical Physics · doi:10.1007/s10955-017-1836-5 · Cited by 393

Network anomaly detection with the restricted Boltzmann machine

Fiore, U., Palmieri, F., Castiglione, A., & De Santis, A. (2013) · Neurocomputing · doi:10.1016/j.neucom.2012.11.050 · Cited by 376

Big Data and supply chain management: A review and bibliometric analysis

Mishra, D., Gunasekaran, A., Papadopoulos, T., & Childe, S. J. (2018) · Annals of Operations Research · doi:10.1007/s10479-016-2236-y · Cited by 382

An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox

Jing, L., Wang, T., Zhao, M., & Wang, P. (2017) · Sensors · doi:10.3390/s17020414 · Cited by 379

Text feature extraction based on deep learning: a review

Liang, H., Sun, X., Sun, Y., & Gao, Y. (2017) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-017-0993-1 · Cited by 373

Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network

Zhang, Y., Li, P., & Wang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2903723 · Cited by 359

Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer

Becker, A. S., Marcon, M., Ghafoor, S., Wurnig, M. C., Frauenfelder, T., & Boss, A. (2017) · Investigative Radiology · doi:10.1097/rli.0000000000000358 · Cited by 352

Advances and opportunities in machine learning for process data analytics

Qin, S. J., & Chiang, L. H. (2019) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.04.003 · Cited by 351

Deep learning-based breast cancer classification through medical imaging modalities: State of the art and research challenges

Murtaza, G., Shuib, L., Abdul Wahab, A. W., Mujtaba, G., Mujtaba, G., Nweke, H. F., Al-garadi, M. A., Zulfiqar, F., Raza, G., & Azmi, N. A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09716-5 · Cited by 342