Innovations in Deep Learning to Predict Individual Risk and Treatment Outcome

HomeRadiologyVol. 307, No. 5 PreviousNext Reviews and CommentaryEditorialInnovations in Deep Learning to Predict Individual Risk and Treatment OutcomeGeorg Langs Georg Langs Author AffiliationsFrom the Computational Imaging Research Laboratory and Christian Doppler Laboratory for Machine Learning Driven Precision Imaging, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.Address correspondence to the author (email: [email protected]).Georg Langs Published Online:Jun 6 2023https://doi.org/10.1148/radiol.231116MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. Jiang X, Zhao H, Saldanha OL, et al. An MRI deep learning model predicts outcome in rectal cancer. Radiology 2023;307(5):e222223. Link, Google Scholar2. Vaswani A, Shazeer N, Parmar N, et al. Attention Is All You Need. In: Advances in Neural Information Processing Systems 30 (NIPS 2017). https://papers.nips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html. Google Scholar3. Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol 2018;18(1):24. Crossref, Medline, Google Scholar4. Perkonigg M, Hofmanninger J, Herold CJ, et al. Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging. Nat Commun 2021;12(1):5678. Crossref, Medline, Google Scholar5. Warstadt A, Bowman SR. What artificial neural networks can tell us about human language acquisition. arXiv 2208.07998 [preprint] https://arxiv.org/abs/2208.07998. Posted August 17, 2022. Accessed May 2023. Google Scholar6. Ricci Lara MA, Echeveste R, Ferrante E. Addressing fairness in artificial intelligence for medical imaging. Nat Commun 2022;13(1):4581. Crossref, Medline, Google Scholar7. McCradden MD, Joshi S, Mazwi M, Anderson JA. Ethical limitations of algorithmic fairness solutions in health care machine learning. Lancet Digit Health 2020;2(5):e221–e223. Crossref, Medline, Google ScholarArticle HistoryReceived: Apr 30 2023Revision requested: May 5 2023Revision received: May 8 2023Accepted: May 9 2023Published online: June 06 2023 FiguresReferencesRelatedDetailsAccompanying This ArticleAn MRI Deep Learning Model Predicts Outcome in Rectal CancerJun 6 2023RadiologyRecommended Articles Deep Learning: A Primer for RadiologistsRadioGraphics2017Volume: 37Issue: 7pp. 2113-2131An MRI Deep Learning Model Predicts Outcome in Rectal CancerRadiology2023Volume: 307Issue: 5Current Applications and Future Impact of Machine Learning in RadiologyRadiology2018Volume: 288Issue: 2pp. 318-328Machine Learning for Hepatocellular Carcinoma Segmentation at MRI: Radiology In TrainingRadiology2022Volume: 304Issue: 3pp. 509-515High-Precision Assessment of Chemoradiotherapy of Rectal Cancer with Near-Infrared Photoacoustic Microscopy and Deep LearningRadiology2021Volume: 299Issue: 2pp. 359-361See More RSNA Education Exhibits Neural Networks in Deep Learning: A Simplified Explanation for RadiologistsDigital Posters2019Uncertainty Estimation in Auto-Segmentation and Image Reconstruction TasksDigital Posters2022Outsmarting AI: What Role Can The Radiologist Play In The Making And Deployment Of Artificial Intelligence ApplicationsDigital Posters2021 RSNA Case Collection Painful Os peroneum syndromeRSNA Case Collection2021Hydrogel infiltration into rectal wallRSNA Case Collection2020Rectal EndometriosisRSNA Case Collection2021 Vol. 307, No. 5 Metrics Altmetric Score PDF download