BriefGPT.xyz
Jun, 2023
可解释回归的原型学习
Prototype Learning for Explainable Regression
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Linde S. Hesse, Nicola K. Dinsdale, Ana I. L. Namburete
TL;DR
本论文提出了ExPeRT:一种可解释的基于原型的回归模型,该模型通过学习的原型的距离和标签的加权平均值进行样本预测,并实现了对成人MR和胎儿超声图像数据集的脑龄预测的最新性能。
Abstract
The lack of
explainability
limits the adoption of
deep learning models
in clinical practice. While methods exist to improve the understanding of such models, these are mainly saliency-based and developed for clas
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