Pre-training in Medical Data: A Survey

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562

A survey on transfer learning

Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387

Sequence to Sequence Learning with Neural Networks

Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766

PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals

Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362

Momentum Contrast for Unsupervised Visual Representation Learning

He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347

Backpropagation Applied to Handwritten Zip Code Recognition

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891

Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008

A simple framework for contrastive learning of visual representations

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv · doi:10.48550/arxiv.2002.05709 · Cited by 10,857

Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data

Quan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J.-C., Saunders, L. D., Beck, C. A., Feasby, T. E., & Ghali, W. A. (2005) · Medical Care · doi:10.1097/01.mlr.0000182534.19832.83 · Cited by 10,669

MIMIC-III, a freely accessible critical care database

Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141

Deep Learning with Differential Privacy

Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578

Representation Learning with Contrastive Predictive Coding

Oord, A. V. D., Li, Y., & Vinyals, O. (2018) · arXiv · doi:10.48550/arxiv.1807.03748 · Cited by 6,242

Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning

Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759

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

Spatial transformer networks

Jaderberg, M., Simonyan, K., Zisserman, A., & Kavukcuoglu, K. (2015) · arXiv · doi:10.48550/arxiv.1506.02025 · Cited by 5,137

DEAP: A Database for Emotion Analysis ;Using Physiological Signals

Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.15 · Cited by 5,093

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842

Greedy layer-wise training of deep networks

Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841

The impact of the MIT-BIH Arrhythmia Database

Moody, G. B., & Mark, R. G. (2001) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.932724 · Cited by 4,834

ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616

Mutational landscape and significance across 12 major cancer types

Kandoth, C., McLellan, M. D., Vandin, F., Ye, K., Niu, B., Lu, C., Xie, M., Zhang, Q., McMichael, J. F., Wyczalkowski, M. A., et al. (2013) · Nature · doi:10.1038/nature12634 · Cited by 4,419

CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep LearningPreprint

Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D. Y., Bagul, A., Langlotz, C. P., Shpanskaya, K. S., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.05225 · Cited by 4,226

The future of digital health with federated learning

Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 4,015 · cited 3×

Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning

Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., et al. (2020) · arXiv · doi:10.48550/arxiv.2006.07733 · Cited by 3,846

Image segmentation using deep learning: A survey

Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3059968 · Cited by 3,796

Exploring Simple Siamese Representation Learning

Chen, X., & He, K. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01549 · Cited by 3,725

Improved Baselines with Momentum Contrastive LearningPreprint

Chen, X., Fan, H., Girshick, R., & He, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04297 · Cited by 3,497

BCI2000: A General-Purpose Brain-Computer Interface (BCI) System

Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N., & Wolpaw, J. R. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827072 · Cited by 3,129

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

Armato, S. G., McLennan, G., Bidaut, L., McNitt‐Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., et al. (2011) · Medical Physics · doi:10.1118/1.3528204 · Cited by 2,887

Clinically applicable deep learning for diagnosis and referral in retinal disease

De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639

Unsupervised Visual Representation Learning by Context Prediction

Doersch, C., Gupta, A., & Efros, A. A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.167 · Cited by 2,629

Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)

Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363547 · Cited by 2,620

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Li, X., Chen, H., Qi, X., Dou, Q., Fu, C., & Heng, P. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2845918 · Cited by 2,530

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., & Joulin, A. (2020) · arXiv · doi:10.48550/arxiv.2006.09882 · Cited by 2,499

Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks

Zheng, W.-L., & Lu, B.-L. (2015) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2015.2431497 · Cited by 2,477

Dataset of breast ultrasound images

Al-Dhabyani, W., Gomaa, M., Khaled, H., & Fahmy, A. (2020) · Data in Brief · doi:10.1016/j.dib.2019.104863 · Cited by 2,405

Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

Apostolopoulos, I. D., & Mpesiana, T. A. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00865-4 · Cited by 2,338

Toward machine emotional intelligence: analysis of affective physiological state

Picard, R. W., Vyzas, E., & Healey, J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.954607 · Cited by 2,334

LXMERT: Learning Cross-Modality Encoder Representations from Transformers

Tan, H., & Bansal, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1514 · Cited by 2,319

UNITER: UNiversal Image-TExt Representation Learning

Chen, Y.-C., Li, L., Yu, L., El Kholy, A., Ahmed, F., Gan, Z., Cheng, Y., & Liu, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58577-8_7 · Cited by 2,055

The eICU Collaborative Research Database, a freely available multi-center database for critical care research

Pollard, T. J., Johnson, A. E. W., Raffa, J. D., Celi, L. A., Mark, R. G., & Badawi, O. (2018) · Scientific Data · doi:10.1038/sdata.2018.178 · Cited by 1,993

Transfer Learning

Torrey, L. A., & SHAVLIK, J. W. (2010) · IGI Global eBooks · doi:10.4018/978-1-60566-766-9.ch011 · Cited by 1,887

Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

Greenspan, H. K., van Ginneken, B., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2553401 · Cited by 1,725

Advances in natural language processing

Hirschberg, J., & Manning, C. D. (2015) · Science · doi:10.1126/science.aaa8685 · Cited by 1,712

A Multimodal Database for Affect Recognition and Implicit Tagging

Soleymani, M., Lichtenauer, J., Pun, T., & Pantic, M. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.25 · Cited by 1,664

An Empirical Study of Training Self-Supervised Vision Transformers

Chen, X., Xie, S., & He, K. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00950 · Cited by 1,648

EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks

Song, T., Zheng, W., Song, P., & Cui, Z. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2817622 · Cited by 1,646

Big Self-Supervised Models are Strong Semi-Supervised LearnersPreprint

Chen, T., Kornblith, S., Swersky, K., Norouzi, M., & Hinton, G. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10029 · Cited by 1,632

Deep Learning Applications in Medical Image Analysis

Ker, J., Wang, L., Rao, J. P., & Lim, C. C. T. (2017) · IEEE Access · doi:10.1109/access.2017.2788044 · Cited by 1,574

PTB-XL, a large publicly available electrocardiography dataset

Wagner, P., Strodthoff, N., Bousseljot, R.-D., Kreiseler, D., Lunze, F. I., Samek, W., & Schaeffter, T. (2020) · Scientific Data · doi:10.1038/s41597-020-0495-6 · Cited by 1,559

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

Setio, A. A. A., Traverso, A., de Bel, T., Berens, M. S. N., van den Bogaard, C., Cerello, P., Chen, H., Dou, Q., Fantacci, M. E., Geurts, B. H. J., et al. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.06.015 · Cited by 1,456

Self-Supervised Learning of Pretext-Invariant Representations

Misra, I., & van der Maaten, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00674 · Cited by 1,435

Medical image analysis using convolutional neural networks: a review

Anwar, S. M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., & Khan, M. K. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1088-1 · Cited by 1,431

DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG

Supratak, A., Dong, H., Wu, C., & Guo, Y. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2721116 · Cited by 1,376

Brain tumor classification using deep CNN features via transfer learning

Deepak, S., & Ameer, P. M. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103345 · Cited by 1,370

The Sleep Heart Health Study: Design, Rationale, and Methods

(1997) · Sleep · doi:10.1093/sleep/20.12.1077 · Cited by 1,331

The National Lung Screening Trial: Overview and Study Design

National Lung Screening Trial Research Team (2010) · Radiology · doi:10.1148/radiol.10091808 · Cited by 1,280

Rethinking ImageNet Pre-Training

He, K., Girshick, R., & Dollár, P. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00502 · Cited by 1,206

DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices

Katsigiannis, S., & Ramzan, N. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2688239 · Cited by 1,135

Predicting cancer outcomes from histology and genomics using convolutional networks

Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Velázquez Vega, J. E., Brat, D. J., & Cooper, L. A. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717139115 · Cited by 1,084

Data-Efficient Image Recognition with Contrastive Predictive Coding

Hénaff, O. J., Srinivas, A., De Fauw, J., Razavi, A., Doersch, C., Eslami, S. M. A., & Oord, A. V. D. (2019) · arXiv · doi:10.48550/arxiv.1905.09272 · Cited by 1,014

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

Deep learning in cancer diagnosis, prognosis and treatment selection

Tran, K. A., Kondrashova, O., Bradley, A., Williams, E. D., Pearson, J. V., & Waddell, N. (2021) · Genome Medicine · doi:10.1186/s13073-021-00968-x · Cited by 932

The National Sleep Research Resource: towards a sleep data commons

Zhang, G., Cui, L., Mueller, R., Tao, S., Kim, M. J., Rueschman, M., Mariani, S., Mobley, D. R., & Redline, S. S. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy064 · Cited by 923

Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification

Hou, L., Samaras, D., Kurç, T., Gao, Y., Davis, J., & Saltz, J. H. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.266 · Cited by 921

Transfusion: Understanding Transfer Learning for Medical ImagingPreprint

Raghu, M., Zhang, C., Kleinberg, J. M., & Samy Bengio, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07208 · Cited by 901

Multi-grade brain tumor classification using deep CNN with extensive data augmentation

Sajjad, M., Khan, S., Muhammad, K., Wu, W., Ullah, A., & Baik, S. W. (2018) · Journal of Computational Science · doi:10.1016/j.jocs.2018.12.003 · Cited by 895

Brain tumor classification for MR images using transfer learning and fine-tuning

Swati, Z. N. K., Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmad, S., & Lu, J. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.05.001 · Cited by 895

CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation

Kavur, A. E., Gezer, N. S., BARIŞ, M. M., Aslan, S., Conze, P.-H., Groza, V., Pham, D. D., Chatterjee, S., Ernst, P., Özkan, S., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101950 · Cited by 893

Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction

Rasmy, L., Xiang, Y., Xie, Z., Tao, C., & Zhi, D. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00455-y · Cited by 886

AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017

Clifford, G. D., Liu, C., Moody, B., Lehman, L.-W. H., Silva, I., Li, Q., Johnson, A. E. W., & Mark, R. G. (2017) · Computing in cardiology · doi:10.22489/cinc.2017.065-469 · Cited by 841

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

Roth, H. R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E. B., & Summers, R. M. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24553-9_68 · Cited by 784

Sparse Feature Learning for Deep Belief Networks

Ranzato, M., Boureau, Y.-L., & LeCun, Y. (2007) · neural information processing systems · Cited by 752

AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups

Miranda-Correa, J. A., Abadi, M. K., Sebe, N., & Patras, I. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2884461 · Cited by 740

Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model

Han, Z., Wei, B., Zheng, Y., Yin, Y., Li, K., & Li, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-04075-z · Cited by 696

Big Self-Supervised Models Advance Medical Image Classification

Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., et al. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00346 · Cited by 685

Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet

Bousseljot, R.-D., Kreiseler, D., & Schnabel, A. (2009) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmte.1995.40.s1.317 · Cited by 660

Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging

Xu, Y., Hosny, A., Zeleznik, R., Parmar, C., Coroller, T. P., Franco, I. “., Mak, R. H., & Aerts, H. J. W. L. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-2495 · Cited by 658

An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection

Liu, F., Liu, C., Zhao, L., Zhang, X., Xiao-ling, W., Xu, X., Liu, Y., Ma, C., Wei, S., He, Z., et al. (2018) · Journal of Medical Imaging and Health Informatics · doi:10.1166/jmihi.2018.2442 · Cited by 623

Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning

Chen, R. J., Chen, C., Li, Y., Chen, T. Y., Trister, A. D., Krishnan, R. G., & Mahmood, F. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01567 · Cited by 621

A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network

Pernas, F. J. D., Martínez‐Zarzuela, M., Antón‐Rodríguez, M., & González‐Ortega, D. (2021) · Healthcare · doi:10.3390/healthcare9020153 · Cited by 607

Multi-Scale Self-Guided Attention for Medical Image Segmentation

Sinha, A., & Dolz, J. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2986926 · Cited by 595

BEHRT: Transformer for Electronic Health Records

Li, Y., Rao, S., Solares, J. R. A., Hassaine, A., Ramakrishnan, R., Canoy, D., Zhu, Y., Rahimi, K., & Salimi-Khorshidi, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-62922-y · Cited by 595

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

Remote Heart Rate Measurement from Face Videos under Realistic Situations

Li, X.-B., Chen, J., Zhao, G.-Y., & Pietikainen, M. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.543 · Cited by 547

ASCERTAIN: Emotion and Personality Recognition Using Commercial Sensors

Subramanian, R., Wache, J., Abadi, M. K., Vieriu, R. L., WINKLER, S. S., & Sebe, N. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2625250 · Cited by 536

The temple university hospital EEG data corpus

Obeid, I., & Picone, J. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00196 · Cited by 528

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

Learning to Learn: Introduction and Overview

Thrun, S., & Pratt, L. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_1 · Cited by 521

Probability of error of some adaptive pattern-recognition machines

Scudder, H. J. (1965) · IEEE Transactions on Information Theory · doi:10.1109/tit.1965.1053799 · Cited by 510

A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor

Noreen, N., Palaniappan, S., Qayyum, A., Ahmad, I., Imran, M. A., & Shoaib, M. (2020) · IEEE Access · doi:10.1109/access.2020.2978629 · Cited by 504

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

Wang, G., Zuluaga, M. A., Li, W., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840695 · Cited by 502

Rotation Equivariant CNNs for Digital Pathology

Veeling, B. S., Linmans, J., Winkens, J., Cohen, T., & Welling, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00934-2_24 · Cited by 476

Deep-learning framework to detect lung abnormality – A study with chest X-Ray and lung CT scan images

Bhandary, A., Prabhu, G. A., Rajinikanth, V., Thanaraj, K. P., Satapathy, S. C., Robbins, D. E., Shasky, C., Zhang, Y., Tavares, J. M. R. S., & Raja, N. S. M. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.013 · Cited by 462

The European ST-T database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography

TADDEI, A., Distante, G., Emdin, M., Pisani, P., Moody, G. B., Zeelenberg, C., & Marchesi, C. (1992) · European Heart Journal · doi:10.1093/oxfordjournals.eurheartj.a060332 · Cited by 439

A convolutional neural network for sleep stage scoring from raw single-channel EEG

Sors, A., Bonnet, S., Mirek, S., Vercueil, L., & Payen, J. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.12.001 · Cited by 435

Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal TransformersPreprint

Huang, Z., Zeng, Z., Liu, B., Fu, D., & Fu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00849 · Cited by 433

A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines

Lu, N., Li, T., Ren, X., & Miao, H. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2601240 · Cited by 422

Recognition of emotions using multimodal physiological signals and an ensemble deep learning model

Yin, Z., Zhao, M., Wang, Y., Yang, J., & Zhang, J. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.12.005 · Cited by 416

Brain tumor classification in MRI image using convolutional neural network

Khan, H. A., Wu, J., Mushtaq, M. U., & Mushtaq, M. U. (2020) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2020328 · Cited by 412

Self-Supervised ECG Representation Learning for Emotion Recognition

Sarkar, P., & Etemad, A. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3014842 · Cited by 411

Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data

Gessert, N., Nielsen, M., Shaikh, M., Werner, R., & Schlaefer, A. (2020) · MethodsX · doi:10.1016/j.mex.2020.100864 · Cited by 409

EEG based multi-class seizure type classification using convolutional neural network and transfer learning

Raghu, S., Sriraam, N., Temel, Y., Rao, S. V., & Kubben, P. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.01.017 · Cited by 387

Digital medicine and the curse of dimensionality

Berisha, V., Krantsevich, C., Hahn, P. R., Hahn, S., Dasarathy, G., Turaga, P., & Liss, J. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00521-5 · Cited by 344

Detecting formal thought disorder by deep contextualized word representations

Sarzyńska‐Wawer, J., Wawer, A., Pawlak, A., Szymanowska, J., Stefaniak, I., Jarkiewicz, M., & Okruszek, Ł. (2021) · Psychiatry Research · doi:10.1016/j.psychres.2021.114135 · Cited by 343

MPED: A Multi-Modal Physiological Emotion Database for Discrete Emotion Recognition

Song, T., Zheng, W., Cheng, L., Zong, Y., Zhang, X., & Cui, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2891579 · Cited by 339

Computer‐aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks

Moon, W. K., Lee, Y., Ke, H.-H., Lee, S. H., Huang, C., & Chang, R. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105361 · Cited by 329

WSISA: Making Survival Prediction from Whole Slide Histopathological Images

Zhu, X., Yao, J., Zhu, F., & Huang, J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.725 · Cited by 320

Rethinking the Value of Labels for Improving Class-Imbalanced LearningPreprint

Yang, Y., & Xu, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07529 · Cited by 320

Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection

Wang, Z., Yin, Y., Shi, J., Fang, W., Li, H., & Wang, X. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_31 · Cited by 318

Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images

Byra, M., Styczyński, G., Szmigielski, C. A., Kalinowski, P., Michałowski, Ł., Paluszkiewicz, R., Ziarkiewicz‐Wróblewska, B., Zieniewicz, K., Sobieraj, P., & Nowicki, A. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1843-2 · Cited by 314

Pre-training of Graph Augmented Transformers for Medication Recommendation

Shang, J., Ma, T., Xiao, C., & Sun, J. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/825 · Cited by 311

Self-Supervised Contrastive Pre-Training for Time Series Via Time-Frequency Consistency

Zhang, X., Zhao, Z., Tsiligkaridis, T., & Žitnik, M. (2022) · neural information processing systems · doi:10.52202/068431-0288 · Cited by 300

Self-supervised learning methods and applications in medical imaging analysis: a survey

Shurrab, S., & Duwairi, R. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.1045 · Cited by 299

Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction

Sharma, L. N., Tripathy, R. K., & Dandapat, S. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2405134 · Cited by 296

Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS)

Santamaria-Granados, L., Munoz-Organero, M., Ramirez-Gonzalez, G., Abdulhay, E., & Arunkumar, N. (2019) · IEEE Access · doi:10.1109/access.2018.2883213 · Cited by 289

Uncovering the structure of clinical EEG signals with self-supervised learning

Banville, H. J., Chehab, O., Hyvärinen, A., Engemann, D. A., & Gramfort, A. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abca18 · Cited by 279

Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural NetworksPreprint

Christ, P. F., Ettlinger, F., Grün, F., Elshaer, M. E. A., Lipková, J., Schlecht, S. J., Ahmaddy, F., Tatavarty, S., Bickel, M., Bilic, P., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.05970 · Cited by 278

Deep convolutional neural network for survival analysis with pathological images

Zhu, X., Yao, J., & Huang, J. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822579 · Cited by 277

brain tumor dataset

Cheng, J. (2015) · Figshare · doi:10.6084/m9.figshare.1512427 · Cited by 262

Unsupervised Pre-Training of Image Features on Non-Curated Data

Caron, M., Bojanowski, P., Mairal, J., & Joulin, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00305 · Cited by 258

Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition

Çimtay, Y., & Ekmekcioǧlu, E. (2020) · Sensors · doi:10.3390/s20072034 · Cited by 254

Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning

Treder, M., Lauermann, J. L., & Eter, N. (2017) · Graefe s Archive for Clinical and Experimental Ophthalmology · doi:10.1007/s00417-017-3850-3 · Cited by 245

Transfer learning for ECG classification

Weimann, K., & Conrad, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-84374-8 · Cited by 239

Multi-Modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training

Moon, J. H., Lee, H., Shin, W., Kim, Y., & Choi, E. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3207502 · Cited by 238

Self-supervised representation learning from 12-lead ECG data

Mehari, T., & Strodthoff, N. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105114 · Cited by 234

Multilevel Weighted Feature Fusion Using Convolutional Neural Networks for EEG Motor Imagery Classification

Amin, S. U., Alsulaiman, M., Muhammad, G., Bencherif, M. A., & Hossain, M. S. (2019) · IEEE Access · doi:10.1109/access.2019.2895688 · Cited by 216

Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning

Nogay, H. S., & Adeli, H. (2020) · European Neurology · doi:10.1159/000512985 · Cited by 210

Convolutional neural networks based efficient approach for classification of lung diseases

Demir, F., Şengür, A., & Bajaj, V. (2019) · Health Information Science and Systems · doi:10.1007/s13755-019-0091-3 · Cited by 208

Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model

Chen, T., Huang, C.-H., Shih, E. S. C., Hu, Y., & Hwang, M. (2019) · iScience · doi:10.1016/j.isci.2020.100886 · Cited by 205

Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction

Bai, W., Chen, C., Tarroni, G., Duan, J., Guitton, F., Petersen, S. E., Guo, Y., Matthews, P. M., & Rueckert, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32245-8_60 · Cited by 191

MMBERT: Multimodal BERT Pretraining for Improved Medical VQA

Khare, Y., Bagal, V., Mathew, M., Devi, A., Priyakumar, U. D., & Jawahar, C. V. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434063 · Cited by 184

Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging

Ayana, G., Dese, K., & Choe, S. (2021) · Cancers · doi:10.3390/cancers13040738 · Cited by 183

Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG

Andreotti, F., Carr, O., Pimentel, M. A. F., Mahdi, A., & Vos, M. D. (2017) · Computing in cardiology · doi:10.22489/cinc.2017.360-239 · Cited by 171

Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning

Phan, H., Chén, O. Y., Koch, P., Lu, Z., McLoughlin, I. V., Mertins, A., & Vos, M. D. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3020381 · Cited by 162

A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks

Pham, T. D. (2020) · Scientific Reports · doi:10.1038/s41598-020-74164-z · Cited by 156

Data From LIDC-IDRI

Armato, S. G., III, McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., et al. (2015) · The Cancer Imaging Archive · doi:10.7937/k9/tcia.2015.lo9ql9sx · Cited by 150

An ensemble of deep neural networks for kidney ultrasound image classification

Sudharson, S., & Kokil, P. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105709 · Cited by 149

Untitled work

(2020) · Cited by 147

Bidirectional Representation Learning From Transformers Using Multimodal Electronic Health Record Data to Predict Depression

Meng, Y., Speier, W. F., Ong, M. K., & Arnold, C. W. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3063721 · Cited by 146

Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?

Lehman, E., Jain, S., Pichotta, K., Goldberg, Y., & Wallace, B. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.73 · Cited by 141

Emotion Analysis for Personality Inference from EEG Signals

Zhao, G., Ge, Y., Shen, B., Wei, X., & Wang, H. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2786207 · Cited by 138

Lung cancer histology classification from CT images based on radiomics and deep learning models

Marentakis, P., Karaiskos, P., Kouloulias, V., Kelekis, N., Argentos, S., Oikonomopoulos, N., & Loukas, C. (2021) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02302-w · Cited by 137

What Makes Transfer Learning Work for Medical Images: Feature Reuse & Other Factors

Matsoukas, C., Haslum, J. F., Sorkhei, M., Söderberg, M. P., & Smith, K. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00901 · Cited by 132

How Useful Is Self-Supervised Pretraining for Visual Tasks?

Newell, A., & Deng, J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00737 · Cited by 131

Deep Learning Computed Tomography

Würfl, T., Ghesu, F. C., Christlein, V., & Maier, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46726-9_50 · Cited by 131

Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series

Tipirneni, S., & Reddy, C. K. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3516367 · Cited by 130

A Novel Multistage Transfer Learning for Ultrasound Breast Cancer Image Classification

Ayana, G., Park, J., Jeong, J.-W., & Choe, S. (2022) · Diagnostics · doi:10.3390/diagnostics12010135 · Cited by 129

Deep Recurrent Neural Networks for ECG Signal DenoisingPreprint

Antczak, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.11551 · Cited by 128

Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks

Conze, P.-H., Kavur, A. E., Gall, É. C.-L., Gezer, N. S., Meur, Y. L., Selver, M. A., & Rousseau, F. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102109 · Cited by 127

Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions

Piotrzkowska‐Wróblewska, H., Dobruch‐Sobczak, K., Byra, M., & Nowicki, A. (2017) · Medical Physics · doi:10.1002/mp.12538 · Cited by 125

Convolutional Neural Networks for Electrocardiogram Classification

Rahhal, M. M. A., Bazi, Y., Zuair, M., Othman, E. M. A., & Benjdira, B. (2018) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-018-0389-7 · Cited by 123

A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks

Kutlu, H., & Avcı, E. (2019) · Sensors · doi:10.3390/s19091992 · Cited by 123

Classification of pathological types of lung cancer from CT images by deep residual neural networks with transfer learning strategy

Wang, S., Dong, L.-Y., Wang, X., & Wang, X. (2020) · Open Medicine · doi:10.1515/med-2020-0028 · Cited by 120

Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework

Sadiq, M. T., Aziz, M. Z., Almogren, A., Yousaf, A., Siuly, S., & Rehman, A. U. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105242 · Cited by 120

Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-supervised Medical Image Segmentation

Li, Y., Chen, J., Xie, X., Ma, K., & Zheng, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59710-8_60 · Cited by 114

Breast Cancer Classification in Ultrasound Images using Transfer Learning

Hijab, A., Rushdi, M. A., Gomaa, M. M. M., & Eldeib, A. (2019) · International Conference on Advances in Biomedical Engineering (ICABME) · doi:10.1109/icabme47164.2019.8940291 · Cited by 110

Bidirectional Representation Learning From Transformers Using Multimodal Electronic Health Record Data to Predict Depression

Meng, Y., Speier, W. F., Ong, M. K., & Arnold, C. W. (2021) · eScholarship (California Digital Library) · Cited by 109

A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound

Gómez‐Flores, W., & Pereira, W. C. D. A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104036 · Cited by 108

Deep Correlational Learning for Survival Prediction from Multi-modality Data

Yao, J., Zhu, X., Zhu, F., & Huang, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66185-8_46 · Cited by 104

BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining

Zhang, Z., Liu, J., & Razavian, N. (2020) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2020.clinicalnlp-1.3 · Cited by 100

Self-supervised Contrastive Learning for EEG-based Sleep Staging

Jiang, X., Zhao, J., Du, B., & Yuan, Z. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533305 · Cited by 95

Quantitative EEG and Neurofeedback in Children and Adolescents

Simkin, D. R., Thatcher, R. W., & Lubar, J. F. (2014) · Child and Adolescent Psychiatric Clinics of North America · doi:10.1016/j.chc.2014.03.001 · Cited by 94

Clinical-BERT: Vision-Language Pre-training for Radiograph Diagnosis and Reports Generation

Yan, B., & Pei, M. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20204 · Cited by 94

Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Cheplygina, V., de Bruijne, M., & Pluim, J. P. W. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.03.009 · Cited by 93

CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and PatientsPreprint

Kiyasseh, D., Zhu, T., & Clifton, D. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.13249 · Cited by 91

Self-supervised Learning is More Robust to Dataset Imbalance

Liu, H., HaoChen, J. Z., Gaidon, A., & Ma, T. (2021) · arXiv · doi:10.48550/arxiv.2110.05025 · Cited by 84

Is Europe prepared to go digital? making the case for developing digital capacity: An exploratory analysis of Eurostat survey data

van Kessel, R., Wong, B. L. H., Rubinić, I., O’Nuallain, E., & Czabanowska, K. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000013 · Cited by 84

Transfer learning for non-image data in clinical research: A scoping review

Ebbehøj, A. L., Thunbo, M. Ø., Andersen, O. E., Glindtvad, M. V., & Hulmán, Á. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000014 · Cited by 83

Fine-tuning convolutional deep features for MRI based brain tumor classification

Ahmed, K. B., Hall, L., Goldgof, D. B., Liu, R., & Gatenby, R. A. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2253982 · Cited by 81

Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration

van Aken, B., Papaioannou, J.-M., Mayrdorfer, M., Budde, K., Gers, F. A., & Loeser, A. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.75 · Cited by 81

A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports

Li, Y., Wang, H., & Luo, Y. (2020) · 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313289 · Cited by 81

Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure AnalysisPreprint

Tang, S., Dunnmon, J., Saab, K. K., Zhang, X., Huang, Q., Dubost, F., Rubin, D. L., & Lee‐Messer, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08336 · Cited by 76

DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction

Zhang, X., Xiao, C., Glass, L. M., & Sun, J. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380181 · Cited by 75

Combining deep neural network and traditional image features to improve survival prediction accuracy for lung cancer patients from diagnostic CT

Paul, R., Hawkins, S. H., Hall, L., Goldgof, D. B., & Gillies, R. J. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844626 · Cited by 75

Subject-Aware Contrastive Learning for Biosignals

Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., & Azemi, E. (2020) · arXiv · doi:10.48550/arxiv.2007.04871 · Cited by 74

A Novel Computer-Aided Diagnosis Framework Using Deep Learning for Classification of Fatty Liver Disease in Ultrasound Imaging

Reddy, D. S., Bharath, R., & Rajalakshmi, P. (2018) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2018.8531118 · Cited by 71

Unsupervised Pre-training Across Image Domains Improves Lung Tissue Classification

Schlegl, T., Ofner, J., & Langs, G. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13972-2_8 · Cited by 67

Brain Tumor Segmentation and Survival Prediction

Agravat, R. R., & Raval, M. S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46640-4_32 · Cited by 66

Knowledge-Enhanced Attributed Multi-Task Learning for Medicine Recommendation

Zhang, Y., Wu, X., Fang, Q., Qian, S., & Xu, C. (2022) · ACM Transactions on Information Systems · doi:10.1145/3527662 · Cited by 64

An incremental learning system for atrial fibrillation detection based on transfer learning and active learning

Shi, H., Wang, H., Qin, C., Zhao, L., & Liu, C. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105219 · Cited by 63

“BioVid Emo DB”: A multimodal database for emotion analyses validated by subjective ratings

Zhang, L., Walter, S., Ma, X., Werner, P., Al-Hamadi, A. K., Traue, H. C., & Gruss, S. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7849931 · Cited by 60

Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation

Li, M., Cai, W., Verspoor, K., Pan, S., Liang, X., & Chang, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02000 · Cited by 60

Self-Supervised Graph Learning With Hyperbolic Embedding for Temporal Health Event Prediction

Lu, C. H., Reddy, C. K. A., & Ning, Y. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3109881 · Cited by 59

Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection

Gao, R., Huo, Y., Bao, S., Tang, Y., Antic, S., Epstein, E. S., Balar, A., Deppen, S., Paulson, A. B., Sandler, K. L., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32692-0_36 · Cited by 55

Transfer Learning for Survival Analysis via Efficient L2,1-Norm Regularized Cox Regression

Li, Y., Wang, L., Wang, J., Ye, J., & Reddy, C. K. A. (2016) · Proceedings · doi:10.1109/icdm.2016.0034 · Cited by 54

Recognition of emotional states using frequency effective connectivity maps through transfer learning approach from electroencephalogram signals

Bagherzadeh, S., Maghooli, K., Shalbaf, A., & Maghsoudi, A. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103544 · Cited by 53

A novel ECG-based real-time detection method of negative emotions in wearable applications

Cheng, Z., Shu, L., Xie, J., & Chen, C. L. P. (2017) · International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) · doi:10.1109/spac.2017.8304293 · Cited by 50

Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning

Sarkar, P., Lobmaier, S. M., Fabre, B. R., González, D., Mueller, A., Frasch, M. G., Antonelli, M. C., & Etemad, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-03376-8 · Cited by 49

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ECG assessment based on neural networks with pretraining

Ribas, V. J., Wojdel, A., Romero, E. E., Ramos, P. M., & Brugada, J. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.08.013 · Cited by 44

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