摘要翻译:
在这项工作中,我们使用了一个深度学习框架,用于基于MR图像和个人信息(即年龄、性别)的帕金森病诊断的同步分类和回归。我们打算通过我们的深度学习框架来促进和增加帕金森病诊断的信心。
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英文标题:
《End-to-End Parkinson Disease Diagnosis using Brain MR-Images by 3D-CNN》
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作者:
Soheil Esmaeilzadeh, Yao Yang, Ehsan Adeli
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最新提交年份:
2018
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述:Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类:Electrical Engineering and Systems Science 电气工程与系统科学
二级分类:Image and Video Processing 图像和视频处理
分类描述:Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括:数学,统计,和感知图像和视频建模和表示;线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据;无损和有损压缩编码;分割、对齐和识别;图像渲染、可视化和打印;计算成像,包括超声、断层和磁共振成像;以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要:
In this work, we use a deep learning framework for simultaneous classification and regression of Parkinson disease diagnosis based on MR-Images and personal information (i.e. age, gender). We intend to facilitate and increase the confidence in Parkinson disease diagnosis through our deep learning framework.
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PDF链接:
https://arxiv.org/pdf/1806.05233