医疗健康领域的数据增强技术：生成对抗网络（GANs）

English | 2023 | ISBN: 3031432045 | 261 Pages | PDF EPUB (True) | 39 MB

Computer-Assisted Diagnostics (CAD) using Convolutional Neural Network (CNN) model has become an important technology in the medical industry, improving the accuracy of diagnostics. However, the lack Magnetic Resonance Imaging (MRI) data leads to the failure of the depth study algorithm. Medical records often different because of the cost of obtaining information and the time-consuming information. In general, clinical data are unreliable, the training of neural network methods to distribute disease across classes does not yield the desired results. Data augmentation is often done by training data to solve problems caused by augmentation tasks such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue.

使用卷积神经网络（CNN）模型的计算机辅助诊断（CAD）在医疗行业已成为一项重要的技术，提高了诊断的准确性。然而，MRI数据的缺乏导致深度学习算法的研究失败。由于获取信息的成本和处理信息的时间问题，医疗记录往往不同。一般来说，临床数据不可靠，用神经网络方法分布疾病类别并没有达到预期效果。通常通过增强训练数据来解决由增强任务引起的问题，如缩放、裁剪、翻转、填充、旋转、平移、仿射变换以及亮度、对比度、饱和度和色调等色彩增强技术。

---

Download from free file storage

 Download from nitroflare.com  Download from rapidgator.net

---

我要反馈

---

本站不对文件进行储存，仅提供文件链接，请自行下载，本站不对文件内容负责，请自行判断文件是否安全，如发现文件有侵权行为，请联系管理员删除。