Machine Learning in Medical Imaging [electronic resource] : 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / edited by Yinghuan Shi, Heung-Il Suk, Mingxia Liu.
Tipo de material:
TextoSeries Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11046Editor: Cham : Springer International Publishing : Imprint: Springer, 2018Edición: 1st ed. 2018Descripción: XII, 409 p. 154 illus., 138 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783030009199Tema(s): Optical data processing | Artificial intelligence | Health informatics | Data mining | Image Processing and Computer Vision | Artificial Intelligence | Health Informatics | Data Mining and Knowledge DiscoveryFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 006.6 | 006.37 Clasificación LoC:TA1630-1650Recursos en línea: Libro electrónico
En: Springer Nature eBookResumen: This book constitutes the proceedings of the 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018. The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.
| Tipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
|---|---|---|---|---|---|---|---|
| Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | 1 | No para préstamo |
Acceso multiusuario
This book constitutes the proceedings of the 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018. The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.
UABC ; Temporal ; 01/01/2021-12/31/2023.
