The literature below has been hand-selected as recommended reading to complement the topics covered during Season 2 of ‘AI-Use Cases: Reasons to do AI with Friends‘, brought to you by ESR Connect and the European Society of Radiology.
General AI papers:
European Society of Radiology (2019) What the radiologist should know about artificial intelligence – an ESR white paper. Insights Imaging. 10(1):44. doi: 10.1186/s13244-019-0738-2.
Choy G. et al. (2018) Current Applications and Future Impact of Machine Learning in Radiology. Radiology. 288(2):318-328. doi: 10.1148/radiol.2018171820.
Saba L. et al (2019) The present and future of deep learning in radiology Eur J Radiol. 114:14-24. doi: 10.1016/j.ejrad.2019.02.038.
Kohli M. et al. (2017) Implementing Machine Learning in Radiology Practice and Research AJR Am J Roentgenol. 208(4):754-760. doi: 10.2214/AJR.16.17224.
Kim D.W. et al (2019) Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers. Korean J Radiol. 20(3):405-410. doi: 10.3348/kjr.2019.0025.
Rajpurkar P. et al. (2017) CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. Available at https://arxiv.org/abs/1711.05225. Accessed 29 January 2020.
Doshi-Velez F. et al. (2019) Evaluating Machine Learning Articles JAMA. 322(18):1777-1779. doi:10.1001/jama.2019.17304.
Breast Cancer:
Yala A. et al. (2019) A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction Radiology. 292(1):60-66. doi: 10.1148/radiol.2019182716.
McKinney S.M. et al. (2020) International evaluation of an AI system for breast cancer screening Nature. 577(7788):89-94. doi: 10.1038/s41586-019-1799-6.
Vogl W.D. et al. (2019) Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features Eur Radiol Exp. 3(1):18. doi: 10.1186/s41747-019-0096-3.
AI in Radiology starting points:
Langs G. et al. (2018) Machine Learning: from radiomics to discovery and routine. Radiologe. 58(Suppl 1):1-6. doi: 10.1007/s00117-018-0407-3.
Lung fibrosis:
Walsh S.L.F. et al. (2018) Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. Lancet Respir Med. 6(11):837-845. doi: 10.1016/S2213-2600(18)30286-8.
Gao M. et al. (2016) Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput Methods Biomech Biomed Eng Imaging Vis. 6(1):1-6. doi: 10.1080/21681163.2015.1124249.
Christe A. et al. (2019) Computer-Aided Diagnosis of Pulmonary Fibrosis Using Deep Learning and CT Images. Invest Radiol. 54(10):627-632. doi: 10.1097/RLI.0000000000000574.
Jacob J. et al. (2017) Mortality prediction in idiopathic pulmonary fibrosis: evaluation of computer-based CT analysis with conventional severity measures. Eur Respir J. 49(1). pii: 1601011. doi: 10.1183/13993003.01011-2016.
Archives:
The Cancer Imaging Archive (2020) The Cancer Imaging Archive. Available at https://www.cancerimagingarchive.net/. Accessed 4 Feb 2020
Grossberg A. et al. (2017) Data from Head and Neck Cancer CT Atlas. The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2017.umz8dv6s
TCGA-OV (2019) The Cancer Imaging Archive. Available at https://wiki.cancerimagingarchive.net/display/Public/TCGA-OV. Accessed 4 February 2020
CT COLONOGRAPHY (2020) The Cancer Imaging Archive. Available at https://wiki.cancerimagingarchive.net/display/Public/CT+COLONOGRAPHY. Accessed 4 February 2020
CodaLab (2017) LiTS – Liver Tumor Segmentation Challenge. CodaLab. Available at https://competitions.codalab.org/competitions/17094. Accessed 4 February 2020
