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Understanding and Interpreting Machine Learning in Medical Image Computing Applications [electronic resource] : First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings / edited by Danail Stoyanov, Zeike Taylor, Seyed Mostafa Kia, Ipek Oguz, Mauricio Reyes, Anne Martel, Lena Maier-Hein, Andre F. Marquand, Edouard Duchesnay, Tommy Löfstedt, Bennett Landman, M. Jorge Cardoso, Carlos A. Silva, Sergio Pereira, Raphael Meier.

Contributor(s): Stoyanov, Danail [editor.] | Taylor, Zeike [editor.] | Kia, Seyed Mostafa [editor.] | Oguz, Ipek [editor.] | Reyes, Mauricio [editor.] | Martel, Anne [editor.] | Maier-Hein, Lena [editor.] | Marquand, Andre F [editor.] | Duchesnay, Edouard [editor.] | Löfstedt, Tommy [editor.] | Landman, Bennett [editor.] | Cardoso, M. Jorge [editor.] | Silva, Carlos A [editor.] | Pereira, Sergio [editor.] | Meier, Raphael [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Image Processing, Computer Vision, Pattern Recognition, and Graphics: 11038Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XVI, 149 p. 60 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030026288.Subject(s): Computer vision | Artificial intelligence | Machine theory | Numerical analysis | Medical informatics | Bioinformatics | Computer Vision | Artificial Intelligence | Formal Languages and Automata Theory | Numerical Analysis | Health Informatics | Computational and Systems BiologyAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.37 Online resources: Click here to access online In: Springer Nature eBookSummary: This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identifythe main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis. .
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This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identifythe main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis. .

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