基于影响的多模态磁共振成像脑肿瘤分割可解释性研究
Influence based explainability of brain tumors segmentation in multimodal Magnetic Resonance Imaging
AI总结：
针对医学图像分割可解释性方法多依赖输入显著性图的问题，本研究将分类任务的TracIn影响可解释性算法扩展至多模态MRI脑肿瘤多类别分割，验证了其忠实性与局部全局解释能力，可用于筛选决策相关特征且适用于类别互斥的语义分割任务。
摘要：
近年来，人工智能已成为医疗应用中的核心工具。尽管发展迅速，但深度神经网络仍是难以解释的黑箱，这是其在临床实践中应用的主要限制。我们聚焦医学图像分割任务，目前已提出的大多数可解释性方法都是通过输入显著性图提供视觉解释。本研究的目标是将最初为分类任务提出的基于影响的可解释性算法TracIn（轨迹影响算法）进行扩展、实现，并测试其在一项具有挑战性的临床问题——多模态磁共振成像中的脑肿瘤多类别分割——中的应用效果。我们通过将网络潜在表征的相似性与TracIn输出相关联，验证了所提算法的忠实性。我们进一步测试了该算法提供局部和全局解释的能力，并认为它可作为一种工具，用于筛选决策过程中使用的最相关特征。该方法可推广应用于所有类别互斥的语义分割任务，而类别互斥是这类任务的标准框架。
英文摘要：
In recent years Artificial Intelligence has emerged as a fundamental tool in medical applications. Despite this rapid development, deep neural networks remain black boxes that are difficult to explain, and this represents a major limitation for their use in clinical practice. We focus on the segmentation of medical images task, where most explainability methods proposed so far provide a visual explanation in terms of an input saliency map. The aim of this work is to extend, implement and test instead an influence-based explainability algorithm, TracIn, proposed originally for classification tasks, in a challenging clinical problem, i.e., multiclass segmentation of tumor brains in multimodal Magnetic Resonance Imaging. We verify the faithfulness of the proposed algorithm linking the similarities of the latent representation of the network to the TracIn output. We further test the capacity of the algorithm to provide local and global explanations, and we suggest that it can be adopted as a tool to select the most relevant features used in the decision process. The method is generalizable for all semantic segmentation tasks where classes are mutually exclusive, which is the standard framework in these cases.
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