Danail Stoyanov; Zeike Taylor; Seyed Mostafa Kia; Understanding and Interpreting Machine Learning in Medical Image Computing Applications 9783030026271

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Автор: Danail Stoyanov; Zeike Taylor; Seyed Mostafa Kia;
Название: Understanding and Interpreting Machine Learning in Medical Image Computing Applications
ISBN: 9783030026271
Издательство: Springer
Классификация:

Массовая культура

Медицинская визуализация

Искусственный интеллект

Машинное обучение

Машинное зрение

Обработка изображений

ISBN-10: 3030026272
Обложка/Формат: Soft cover
Страницы: 149
Вес: 0.27 кг.
Дата издания: 2018
Серия: Image Processing, Computer Vision, Pattern Recognition, and Graphics
Язык: English
Издание: 1st ed. 2018
Иллюстрации: 60 illustrations, black and white; xvi, 149 p. 60 illus.
Размер: 234 x 156 x 9
Читательская аудитория: Professional & vocational
Основная тема: Computer Science
Подзаголовок: First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: 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 Alzheimers disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the 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.

Understanding and Interpreting Machine Learning in Medical Image Computing Applications, Danail Stoyanov; Zeike Taylor; Seyed Mostafa Kia;