计算机科学
基本事实
骨料(复合)
机器学习
过程(计算)
人工智能
数据挖掘
操作系统
复合材料
材料科学
作者
Nicolas Captier,Fanny Orlhac,Narinée Hovhannisyan-Baghdasarian,Marie Luporsi,Nicolas Girard,Irène Buvat
标识
DOI:10.2967/jnumed.124.267434
摘要
Explaining the decisions made by a radiomic model is of significant interest, as it can provide valuable insights into the information learned by complex models and foster trust in well-performing ones, thereby facilitating their clinical adoption. Promising radiomic approaches that aggregate information from multiple regions within an image currently lack suitable explanation tools that could identify the regions that most significantly influence their decisions. Here we present a model- and modality-agnostic tool (RadShap, https://github.com/ncaptier/radshap), based on Shapley values, that explains the predictions of multiregion radiomic models by highlighting the contribution of each individual region.
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