Explainable Deep Learning Methodologies for Biomedical Images Classification

Marcello Di Giammarco, F. Mercaldo, Fabio Martinelli, A. Santone
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引用次数: 1

Abstract

Often when we have a lot of data available we can not give them an interpretability and an explainability such as to be able to extract answers, and even more so diagnosis in the medical field. The aim of this contribution is to introduce a way to provide explainability to data and features that could escape even medical doctors, and that with the use of Machine Learning models can be categorized and "explained".
生物医学图像分类的可解释深度学习方法
通常,当我们有很多可用的数据时,我们不能给它们一个可解释性和可解释性,例如能够提取答案,甚至在医学领域的诊断。这一贡献的目的是引入一种方法,为甚至连医生都无法解释的数据和特征提供可解释性,并且通过使用机器学习模型可以对其进行分类和“解释”。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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