PDF(Pubmed)
Abstract：
Recent developments in technology and research have offered a wide variety of new techniques for image and data analysis within the medical field. Medical research helps doctors and researchers acquire not only knowledge about health and new diseases, but also techniques of prevention and treatment. In particular, radiomic analysis is mainly used to extract quantitative data from medical images and to build a model strong enough to diagnose focal diseases. However, finding a model capable to fit all patient situations is not an easy task. In this paper frame prediction models and classification models are reported in order to predict the evolution of a given data series and determine whether an anomaly exists or not. This article also shows how to build and make use of a convolutional neural network-based architecture aiming to accomplish prediction task for medical images, not only using common computer tomography scans, but also 3D volumes.
摘要：
技术和研究的最新发展为医学领域的图像和数据分析提供了各种各样的新技术。医学研究不仅可以帮助医生和研究人员获得有关健康和新疾病的知识，还有预防和治疗的技术。特别是,影像组学分析主要用于从医学图像中提取定量数据，并建立足够强大的模型来诊断局灶性疾病。然而,找到一个能够适合所有患者情况的模型并不是一件容易的事。本文报告了框架预测模型和分类模型，以预测给定数据序列的演变并确定是否存在异常。本文还展示了如何构建和利用基于卷积神经网络的架构，旨在完成医学图像的预测任务，不仅使用普通的计算机断层扫描，还有3D体积。
