To overcome the shortcomings of radiomics methods, we developed a more advanced method, called deep learning-based radiomics (DLR). DLR obtains radiomics features by normalizing the information from a deep neural network designed for image segmentation. The main assumption of DLR is that once the image has been segmented accurately by the deep neural network, all the information about the segmented region will have already been installed in the network. Unlike current radiomics methods, in DLR, the high-throughput image features are directly extracted from the deep neural network. Because DLR does not involve extra feature extrac- tion operations, no extra errors will be introduced into the radiomics analysis because of feature calculations. The effectiveness of features is related only to the quality of segmentation. If the tumor has been segmented precisely, the accuracy and effectiveness of the image features can be guaranteed 解释
时间: 2024-04-05 12:32:11 浏览: 23
这段话提到了一个新的医学影像分析方法,叫做基于深度学习的放射组学(DLR)。与现有的放射组学方法不同,DLR直接从深度神经网络中提取高通量的图像特征,而不需要进行额外的特征提取操作,从而避免了因特征计算而引入额外的误差。DLR的主要假设是,一旦图像被深度神经网络准确地分割出来,所有与分割区域相关的信息都已经被嵌入到网络中。因此,DLR的特征有效性与分割的质量密切相关。如果肿瘤被准确地分割了,那么图像特征的准确性和有效性就能得到保证。
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Third-party cookie blocking is a measure taken by web browsers to enhance user privacy and security. It can, however, cause some issues for websites that rely on third-party cookies for various functionalities such as tracking, authentication, and personalized content. To address this problem, there are a few possible solutions:
1. First-party cookies: Instead of relying on third-party cookies, websites can use first-party cookies to store user data. First-party cookies are set by the website itself and are not affected by the blocking of third-party cookies.
2. Server-side tracking: Websites can switch to server-side tracking methods, where user data is processed and stored on the server rather than relying on client-side cookies. This approach can help overcome the limitations imposed by third-party cookie blocking.
3. Consent management platforms: Implementing a consent management platform allows websites to obtain user consent for data collection and tracking. By obtaining explicit consent, websites can continue to use cookies, including third-party cookies, while complying with privacy regulations.
4. Alternative tracking technologies: Websites can explore alternative tracking technologies such as local storage, session storage, or browser fingerprinting. These methods have their own limitations and considerations, so careful implementation and consideration of privacy implications are necessary.
The Non-IID Data Quagmire of Decentralized Machine Learning
Decentralized machine learning is a promising approach to train models on distributed data without the need for data sharing. However, one of the major challenges in this approach is dealing with non-IID (non-independent and identically distributed) data.
When data is non-IID, it means that the data samples across different participants have different statistical properties. This can happen when the data is collected from different sources, or when different participants have different data collection processes.
In decentralized machine learning, non-IID data can lead to several problems. For example, it can cause communication overheads, as participants need to exchange more data to train the model. It can also lead to slower convergence rates, as the model may struggle to generalize to new data that is different from the training data.
To overcome these challenges, researchers have proposed several techniques, such as data normalization, data augmentation, and model personalization. These techniques aim to make the data more IID-like and improve the performance of decentralized machine learning.
Overall, dealing with non-IID data is a significant challenge in decentralized machine learning, but with the right techniques and approaches, it is possible to overcome this quagmire and achieve accurate and efficient training on distributed data.
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