Ressources sur l’intelligence artificielle, l’apprentissage automatique, et l’apprentissage profond

Lectures suggérées pour les radiologistes

Introduction générale en matière d’intelligence artificielle, d’apprentissage automatique, et d’apprentissage profond

Implementing Machine Learning in Radiology Practice and Research¹

Machine Learning for Medical Imaging²

Big Data and Machine Learning—Strategies for Driving This Bus: A Summary of the 2016 Intersociety Summer Conference³

Big Data, Machine Learning, and Clinical Medicine⁴

Artificial Intelligence in Healthcare : Past, Present and Future⁵

^{Imaging Informatics – Year in Review 2017}

Information technique de base en matière d’intelligence artificielle, d’apprentissage automatique, et d’apprentissage profond

Deep Learning in Medical Image Analysis⁶

Machine Learning and Radiology⁷

A Few Useful Things to Know about Machine Learning⁸

Medical Image Data and Datasets in the Era of Machine Learning⁹

Toolkits and Libraries for Deep Learning¹⁰

Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique¹¹

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions¹²

How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?¹³

A Roadmap Towards Machine Intelligence¹⁴

Deep Learning for Computational Biology¹⁵

Deep Learning for Healthcare: Review, Opportunities and Challenges¹⁶

Examples d’apprentissage automatique en radiologie et en soins de santé

Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework¹⁷

Using Machine Learning and Natural Language Processing Algorithms to Automate the Evaluation of Clinical Decision Support in Electronic Medical Record Systems¹⁸

Liste de références

¹ Kohli M, Prevedello LM, Filice RW, Geis JR. Implementing Machine Learning in Radiology Practice and Research. AJR Am J Roentgenol. 2017 Apr;208(4):754-760 https://www.ncbi.nlm.nih.gov/pubmed/28125274

² Erickson BJ, Korfiatis P, Zeynettin A, Kline TL. Machine Learning for Medical Imaging.  Radiographics 2017 Mar-Apr;37(2):505-515   http://pubs.rsna.org/doi/full/10.1148/rg.2017160130

³ Kruskal JB, Berkowitz S, Geis JR, Kim W, Dreyer K. Big Data and Machine Learning—Strategies for Driving This Bus: A Summary of the 2016 Intersociety Summer Conference. J Am Coll Radiol. 2017 Jun;14(6):811-817  https://www.ncbi.nlm.nih.gov/pubmed/28372961

⁴ Obermeyer Z, Emanuel E. Predicting the Future — Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016 Sep 29; 375(13): 1216–1219 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5070532/

⁵ Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology 2017;0: e000101. http://svn.bmj.com/content/early/2017/06/21/svn-2017-000101

⁶ Shen D, Wu G, Suk H-I  Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng. 2017 Jun 21; 19: 221-248. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5479722/

⁷ Wang S, Summers R. Machine Learning and Radiology. Med Image Anal. 2012 Jul https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3372692/

⁸ Domingos, P. A Few Useful Things to Know about Machine Learning. https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf

⁹ Kohli, M.D., Summers, R.M. & Geis, J. J Digit Imaging. May 2017.  https://link.springer.com/article/10.1007%2Fs10278-017-9976-3

¹⁰ Erickson BJ, Korfiatis P, Akkus Z, Kline T, Philbrick K. Toolkits and Libraries for Deep Learning. J Digit Imaging (2017) https://link.springer.com/article/10.1007/s10278-017-9965-6

¹¹ Greenspan H, van Ginneken B, Summers RM. Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Transactions on Medical Imaging (Volume: 35, Issue: 5, May 2016) http://ieeexplore.ieee.org/document/7463094/

¹² Akkus, Z., Galimzianova, A., Hoogi, A. et al. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.  J Digit Imaging (2017) https://link.springer.com/article/10.1007/s10278-017-9983-4

¹³ Cho J, Lee K, Shin E, Choy G, Do S. How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? https://arxiv.org/pdf/1511.06348.pdf

¹⁴ Mikolov T, Joulin A, Baroni M. A Roadmap towards Machine Intelligence https://arxiv.org/pdf/1511.08130.pdf

¹⁵ Angermueller C, Parnamaa T, Parts L, Stegle O. Deep learning for computational biology. Mol Syst Biol. 2016;12(7):878. https://www.ncbi.nlm.nih.gov/pubmed/27474269

¹⁶ Miotto R, Wang F, Wang S, Jiang X, Dudley J. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2017 http://dudleylab.org/wp-content/uploads/2017/05/Deep-learning-for-healthcare-review-opportunities-and-challenges.pdf

¹⁷ Oakden-Rayner L, Carneiro G, Bessen T et al.  Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework. Scientific Reports 7, Article number: 1648 (2017) https://www.nature.com/articles/s41598-017-01931-w

¹⁸ Szlosek DA, Ferrett J. Using Machine Learning and Natural Language Processing Algorithms to Automate the Evaluation of Clinical Decision Support in Electronic Medical Record Systems EGEMS (Wash DC). 2016 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5019306/