Lectures suggérées sur l’IA

Introduction générale à l'intelligence artificielle, à l'apprentissage automatique et à l'apprentissage profond en radiologie

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/