首页Deep learning radiomic nomogram

Deep learning radiomic nomogram

时间: 2023-08-27 20:05:52 浏览: 99

deep learning

'Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.' -- Elon Musk, co-chair of OpenAI; co-founder and CEO of Tesla and SpaceX, Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning., The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models., Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning Radiomic Nomogram（DLRN）是一种基于深度学习和放射学图像学的方法，用于预测肿瘤生存、治疗反应和其他临床结果。DLRN利用计算机视觉和机器学习技术，从医学影像中提取大量的定量特征（radiomic features），并将其与临床数据结合起来建立预测模型。 DLRN的核心思想是将医学影像中的图像特征与患者的临床数据相结合，通过训练深度神经网络来学习这些特征与预测结果之间的关系。最终，DLRN可以生成一个可视化的预测模型，称为nomogram，用于评估患者的预后风险或治疗反应。 DLRN在放射学领域有着广泛的应用，可用于诊断、预后评估和治疗决策等方面。它可以帮助放射科医生更准确地评估肿瘤患者的风险，并为个体化治疗提供参考依据。需要注意的是，DLRN仅仅是一种方法或技术，具体的实施和应用需要结合具体的研究或临床场景来进行。

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