Verificar estado da encomenda

Faça parte de uma comunidade de amantes de livros de todo o mundo e tenha acesso a uma série de benefícios. Crie uma conta gratuitamente

Correio DHL 7.99 € Correio DPD 4.49 € Ponto DPD 3.99 € Correio GLS 5.49 € Correio MRW 5.49 € Ponto GLS 4.49 €

Contacto

Como comprar

Ajuda

A minha conta

▸ Vazio :-(

Machine Learning for Multimodal Healthcare Data

Name: Machine Learning for Multimodal Healthcare Data
Brand: Springer
SKU: 44992446
Price: 74.15 EUR
Availability: InStock
Author: Andreas K. Maier
ISBN: 9783031476792

First International Workshop, ML4MHD 2023, Honolulu, Hawaii, USA, July 29, 2023, Proceedings

Língua

Inglês

E-book Adobe ePub DRM

Editoras Springer, novembro 2023

This book constitutes the proceedings of the First International Workshop on Machine Learning for Mu... Descrição completa

Código Libristo: 44992446

179 b

74.15 €

Em stock Imediatamente descarregável

Os clientes também compraram

Partielle Differentialgleichungen der Geometrie und der Physik. Bd.2 Friedrich Sauvigny

Capa mole

43.21 €

Sirca Kösk

Capa mole

10.74 €

Aprovisionamiento y montaje para servicios de catering MOTTO LÓPEZ

Capa mole

24.44 €

Aktualizaciya zhiznennogo poznavatel'nogo opyta studentov vysshej shkoly Nadezhda Solov'eva

Capa mole

25.66 €

Les droits du justiciable face a la justice penale Vanessa Franssen

Adobe ePub DRM

90.18 €

El Heroe de Eleanor Jill Barnett

Adobe ePub DRM

3.13 €

Entwicklung des Homeshopping im System des Einzelhandels Steffen Böhm

Capa mole

36.00 €

This book constitutes the proceedings of the First International Workshop on Machine Learning for Multimodal Healthcare Date, ML4MHD 2023, held in Honolulu, Hawaii, USA, in July 2023. The 18 full papers presented were carefully reviewed and selected from 30 submissions. The workshop's primary objective was to bring together experts from diverse fields such as medicine, pathology, biology, and machine learning. With the aim to present novel methods and solutions that address healthcare challenges, especially those that arise from the complexity and heterogeneity of patient data.