Titlebook: Explainable Machine Learning in Medicine; Karol Przystalski,Rohit M. Thanki Book 2024 The Editor(s) (if applicable) and The Author(s), und

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书目名称Explainable Machine Learning in Medicine影响因子(影响力)学科排名




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书目名称Explainable Machine Learning in Medicine网络公开度学科排名




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书目名称Explainable Machine Learning in Medicine被引频次学科排名




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书目名称Explainable Machine Learning in Medicine年度引用学科排名




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书目名称Explainable Machine Learning in Medicine读者反馈学科排名

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Medical Tabular Data,simple example of this type of data. We have the weight, age, and height values divided by the person to which they are assigned. Such data might be too simple to be used with a deep neural network or even most shallow methods, but we can use methods such as linear regression to see the correlation

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,Natural Language Processing for Medical Data Analysis,ledge and the terms that in the case of medicine can be in Latin. This means that most of the publicly available language models are not trained to recognize the medical terms. For each NLP exercise, most of the models are developed in English.

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,Computer Vision for Medical Data Analysis,he medical diagnostics on a daily basis. The most typical are X-ray and ultrasound scans. Some General Practitioners have a dermatoscope to take pictures of the skin. Similarly, thermography can be used to take kind of heatmap images of our body. If the diagnosis of a disease is more complex, magnet

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Critique as a Notion of Spiritualityn observe how the observed organ changes its behavior over time, making it possible to recognize different types of anomalies or diseases compared to data captured in one time, such as typical MRI scans.

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