Quantitative Evaluations on Saliency Methods: An Experimental Study

Crowdsourcing Evaluation of Saliency-based XAI Methods

Understanding the reasons behind the predictions made by deep neural networks is critical for gaining human trust in many important applications, which is reflected in the increasing demand for explainability in AI (XAI) in recent years. Saliency-based feature attribution methods, which highlight important parts of images that contribute to decisions by classifiers, are often used as XAI methods, especially in the field of computer vision. In order to compare various saliency-based XAI methods quantitatively, several approaches for automated evaluation schemes have been proposed; however, ther…

A Psychological Theory of Explainability

The goal of explainable Artificial Intelligence (XAI) is to generate human-interpretable explanations, but there are no computationally precise theories of how humans interpret AI generated explanations. The lack of theory means that validation of XAI must be done empirically, on a case-by-case basis, which prevents systematic theory-building in XAI. We propose a psychological theory of how humans draw conclusions from saliency maps, the most common form of XAI explanation, which for the first time allows for precise prediction of explainee inference conditioned on explanation. Our theory posi…

LAYER：一种用于解码肌筋膜低背痛组织层驱动因素的可解释人工智能框架

LAYER: A Quantitative Explainable AI Framework for Decoding Tissue-Layer Drivers of Myofascial Low Back Pain

LAYER框架通过分析多层组织在3D超声中的贡献，揭示非肌肉组织对肌筋膜疼痛预测的重要性，挑战传统肌肉中心范式。

SIDU: Similarity Difference and Uniqueness Method for Explainable AI

A new brand of technical artificial intelligence ( Explainable AI ) research has focused on trying to open up the 'black box' and provide some explainability. This paper presents a novel visual explanation method for deep learning networks in the form of a saliency map that can effectively localize entire object regions. In contrast to the current state-of-the art methods, the proposed method shows quite promising visual explanations that can gain greater trust of human expert. Both quantitative and qualitative evaluations are carried out on both general and clinical data sets to confirm the e…

将脑年龄预测问题重构为一种更具可解释性和定量性的方法

Reframing the Brain Age Prediction Problem to a More Interpretable and Quantitative Approach

针对现有脑年龄预测深度学习模型仅能全局预测、依赖的显著性图难解释且不可跨样本比较的问题，本研究将MR图像脑年龄预测重构为体素级图像到图像回归任务，验证了该方法更具可解释性与定量优势。

Abs-CAM: A Gradient Optimization Interpretable Approach for Explanation of Convolutional Neural Networks

The black-box nature of Deep Neural Networks (DNNs) severely hinders its performance improvement and application in specific scenes. In recent years, class activation mapping-based method has been widely used to interpret the internal decisions of models in computer vision tasks. However, when this method uses backpropagation to obtain gradients, it will cause noise in the saliency map, and even locate features that are irrelevant to decisions. In this paper, we propose an Absolute value Class Activation Mapping-based (Abs-CAM) method, which optimizes the gradients derived from the backpropaga…