Fetching the paper…
Reading the bibliography…
Pre-training has shown success in different areas of machine learning, such as Computer Vision, Natural Language Processing (NLP), and medical imaging.
A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals,” circulation
2000
Earlier work this paper cites.
T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings
2013
Earlier work this paper cites.
B. J. Wells, K. M. Chagin, A. S. Nowacki, and M. W. Kattan, “Strategies for handling missing data in electronic health record derived data,” Egems
2013
Earlier work this paper cites.
J. Pennington, R. Socher, and C. D. Manning, “Glove: Global vectors for word representation,” in Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980
2014
Earlier work this paper cites.
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2016
Earlier work this paper cites.
A. E. Johnson, T. J. Pollard, L. Shen, L.-w. H. Lehman, M. Feng, M. Ghassemi, B. Moody, P. Szolovits, L. Anthony Celi, and R. G. Mark, “Mimic-iii, a freely accessible critical care database,” Scientific data
2016
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” 2016
2016
Earlier work this paper cites.
P. Bojanowski and A. Joulin, “Unsupervised learning by predicting noise,” in International Conference on Machine Learning
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. E. van Timmeren, R. T. Leijenaar, W. van Elmpt, B. Reymen, and P. Lambin, “Feature selection methodology for longitudinal cone-beam ct radiomics,” Acta oncologica
2017
Earlier work this paper cites.
C. Xiao, E. Choi, and J. Sun, “Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review,” Journal of the American Medical Informatics Association
2018
Earlier work this paper cites.
M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “Deep contextualized word representations,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
2018
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
M. Caron, P. Bojanowski, A. Joulin, and M. Douze, “Deep clustering for unsupervised learning of visual features,” in Proceedings of the European conference on computer vision (ECCV)
2018
Earlier work this paper cites.
N. Komodakis and S. Gidaris, “Unsupervised representation learning by predicting image rotations,” in International Conference on Learning Representations (ICLR)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
S. Parisot, S. I. Ktena, E. Ferrante, M. Lee, R. Guerrero, B. Glocker, and D. Rueckert, “Disease prediction using graph convolutional networks: application to autism spectrum disorder and alzheimer’s disease,” Medical image analysis
2018
Earlier work this paper cites.
G. Vivar, A. Zwergal, N. Navab, and S.-A. Ahmadi, “Multi-modal disease classification in incomplete datasets using geometric matrix completion,” in Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities
2018
Earlier work this paper cites.
T. J. Pollard, A. E. Johnson, J. D. Raffa, L. A. Celi, R. G. Mark, and O. Badawi, “The eicu collaborative research database, a freely available multi-center database for critical care research,” Scientific data
2018
Earlier work this paper cites.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in International Conference on Learning Representations
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding.,” in NAACL-HLT (1)
2019
Earlier work this paper cites.
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” in Advances in Neural Information Processing Systems
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
W. Bai, C. Chen, G. Tarroni, J. Duan, F. Guitton, S. E. Petersen, Y. Guo, P. M. Matthews, and D. Rueckert, “Self-supervised learning for cardiac mr image segmentation by anatomical position prediction,” in International Conference on Medical Image Computing and Computer-Assisted Intervention
2019
Cited alongside, same era.
L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara, and D. Rueckert, “Self-supervised learning for medical image analysis using image context restoration,” Medical image analysis
2019
Cited alongside, same era.
J. Shang, T. Ma, C. Xiao, and J. Sun, “Pre-training of graph augmented transformers for medication recommendation,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19
2019
Cited alongside, same era.
T. Zebin, S. Rezvy, and T. J. Chaussalet, “A deep learning approach for length of stay prediction in clinical settings from medical records,” in 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
A. Johnson, L. Bulgarelli, T. Pollard, S. Horng, L. A. Celi, and R. Mark, “Mimic-iv,” PhysioNet. Available online at: https://physionet. org/content/mimiciv/1.0/(accessed August 23, 2021)
2020
Later among the works it cites.
2021
Later among the works it cites.
X. Liu, F. Zhang, Z. Hou, L. Mian, Z. Wang, J. Zhang, and J. Tang, “Self-supervised learning: Generative or contrastive,” IEEE Transactions on Knowledge and Data Engineering
2021
Later among the works it cites.
X. Han, Z. Zhang, N. Ding, Y. Gu, X. Liu, Y. Huo, J. Qiu, Y. Yao, A. Zhang, L. Zhang, et al
2021
Later among the works it cites.
L. Rasmy, Y. Xiang, Z. Xie, C. Tao, and D. Zhi, “Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction,” NPJ digital medicine
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
A. Kazi, S. Shekarforoush, K. Kortuem, S. Albarqouni, N. Navab, et al
2019
Cited alongside, same era.
M. A. Reyna, C. Josef, S. Seyedi, R. Jeter, S. P. Shashikumar, M. B. Westover, A. Sharma, S. Nemati, and G. D. Clifford, “Early prediction of sepsis from clinical data: the physionet/computing in cardiology challenge 2019,” in 2019 Computing in Cardiology (CinC)
2019
Cited alongside, same era.
A. Kazi, S. Shekarforoush, S. Arvind Krishna, H. Burwinkel, G. Vivar, K. Kortüm, S.-A. Ahmadi, S. Albarqouni, and N. Navab, “Inceptiongcn: receptive field aware graph convolutional network for disease prediction,” in International Conference on Information Processing in Medical Imaging
2019
Cited alongside, same era.
J. Valenchon and M. Coates, “Multiple-graph recurrent graph convolutional neural network architectures for predicting disease outcomes,” in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
A. E. Johnson, T. J. Pollard, S. J. Berkowitz, N. R. Greenbaum, M. P. Lungren, C.-y. Deng, R. G. Mark, and S. Horng, “Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports,” Scientific data
2019
Cited alongside, same era.
I. Landi, B. S. Glicksberg, H.-C. Lee, S. Cherng, G. Landi, M. Danieletto, J. T. Dudley, C. Furlanello, and R. Miotto, “Deep representation learning of electronic health records to unlock patient stratification at scale,” NPJ digital medicine
2020
Cited alongside, same era.
2021
Later among the works it cites.
C. Pang, X. Jiang, K. S. Kalluri, M. Spotnitz, R. Chen, A. Perotte, and K. Natarajan, “Cehr-bert: Incorporating temporal information from structured ehr data to improve prediction tasks,” in Machine Learning for Health
2021
Later among the works it cites.
M. McDermott, B. Nestor, E. Kim, W. Zhang, A. Goldenberg, P. Szolovits, and M. Ghassemi, “A comprehensive ehr timeseries pre-training benchmark,” in Proceedings of the Conference on Health, Inference, and Learning
2021
Later among the works it cites.
D. Ahmedt-Aristizabal, M. A. Armin, S. Denman, C. Fookes, and L. Petersson, “Graph-based deep learning for medical diagnosis and analysis: Past, present and future,” Sensors
2021
Later among the works it cites.
Y. Lu, X. Jiang, Y. Fang, and C. Shi, “Learning to pre-train graph neural networks,” AAAI, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
X. Wang, S. Yang, J. Zhang, M. Wang, J. Zhang, J. Huang, W. Yang, and X. Han, “Transpath: Transformer-based self-supervised learning for histopathological image classification,” in International Conference on Medical Image Computing and Computer-Assisted Intervention
2021
Later among the works it cites.
C. Ying, T. Cai, S. Luo, S. Zheng, G. Ke, D. He, Y. Shen, and T.-Y. Liu, “Do transformers really perform badly for graph representation?,” Advances in Neural Information Processing Systems
2021
Later among the works it cites.
2021
Later among the works it cites.
N. Pötsch, M. Dietzel, P. Kapetas, P. Clauser, K. Pinker, S. Ellmann, M. Uder, T. Helbich, and P. A. Baltzer, “An ai classifier derived from 4d radiomics of dynamic contrast-enhanced breast mri data: potential to avoid unnecessary breast biopsies,” European radiology
2021
Later among the works it cites.
R. Zheng, C. Shi, C. Wang, N. Shi, T. Qiu, W. Chen, Y. Shi, and H. Wang, “Imaging-based staging of hepatic fibrosis in patients with hepatitis b: A dynamic radiomics model based on gd-eob-dtpa-enhanced mri,” Biomolecules
2021
Later among the works it cites.
Q.-P. Ma, X.-l. He, K. Li, J.-f. Wang, Q.-J. Zeng, E.-J. Xu, X.-q. He, S.-y. Li, W. Kun, R.-Q. Zheng, et al
2021
Later among the works it cites.
S. Park, S. Bae, J. Kim, T. Kim, and E. Choi, “Graph-text multi-modal pre-training for medical representation learning,” in Proceedings of the Conference on Health, Inference, and Learning
2022
Closest in time.
A. Kazi, L. Cosmo, S.-A. Ahmadi, N. Navab, and M. Bronstein, “Differentiable graph module (dgm) for graph convolutional networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2022
Closest in time.
M. Ghorbani, A. Kazi, M. S. Baghshah, H. R. Rabiee, and N. Navab, “Ra-gcn: Graph convolutional network for disease prediction problems with imbalanced data,” Medical Image Analysis
2022
Closest in time.
Y. Xie, Z. Xu, J. Zhang, Z. Wang, and S. Ji, “Self-supervised learning of graph neural networks: A unified review,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2022
Closest in time.
M. N. Agrawal, H. Lang, M. Offin, L. Gazit, and D. Sontag, “Leveraging time irreversibility with order-contrastive pre-training,” in International Conference on Artificial Intelligence and Statistics
2022
Closest in time.
Accessed: 2022-07-04
ml glossary, “Cross-entropy.” https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html#cross-entropy · 2022
Closest in time.
Accessed: 2022-07-04
ml glossary, “Mse (l2).” https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html#mse-l2 · 2022
Closest in time.
L. R. Soenksen, Y. Ma, C. Zeng, L. Boussioux, K. Villalobos Carballo, L. Na, H. M. Wiberg, M. L. Li, I. Fuentes, and D. Bertsimas, “Integrated multimodal artificial intelligence framework for healthcare applications,” NPJ Digital Medicine
2022
Closest in time.
J. P. Cohen, J. D. Viviano, P. Bertin, P. Morrison, P. Torabian, M. Guarrera, M. P. Lungren, A. Chaudhari, R. Brooks, M. Hashir, et al
2022
Closest in time.