With an increase in deep learning-based methods, the call for explainability
of such methods grows, especially in high-stakes decision making areas such as
medical image analysis. This survey presents an overview of eXplainable
Artificial Intelligence (XAI) used in deep learning-based medical image
analysis. A framework of XAI criteria is introduced to classify deep
learning-based medical image analysis methods. Papers on XAI techniques in
medical image analysis are then surveyed and categorized according to the
framework and according to anatomical location. The paper concludes with an
outlook of future opportunities for XAI in medical image analysis.

本文调查了可解释人工智能 (XAI) 在基于深度学习的医学图像分析中的应用，提出了一个 XAI 标准框架来分类这些基于深度学习的方法，根据解释方法和解释结构将应用于医学图像分析中的 XAI 技术进行了分类，并按解剖位置进行了整理，并给出了未来在医学图像分析中 XAI 的机会的展望。

基于深度学习的医学图像分析中的可解释人工智能（XAI）

Explainable artificial intelligence (XAI) in deep learning-based medical  image analysis

The anatomical location of imaging features is of crucial importance for
accurate diagnosis in many medical tasks. Convolutional neural networks (CNN)
have had huge successes in computer vision, but they lack the natural ability
to incorporate the anatomical location in their decision making process,
hindering success in some medical image analysis tasks.
In this paper, to integrate the anatomical location information into the
network, we propose several deep CNN architectures that consider multi-scale
patches or take explicit location features while training. We apply and compare
the proposed architectures for segmentation of white matter hyperintensities in
brain MR images on a large dataset. As a result, we observe that the CNNs that
incorporate location information substantially outperform a conventional
segmentation method with hand-crafted features as well as CNNs that do not
integrate location information. On a test set of 46 scans, the best
configuration of our networks obtained a Dice score of 0.791, compared to 0.797
for an independent human observer. Performance levels of the machine and the
independent human observer were not statistically significantly different
(p-value=0.17).

本文研究了使用多尺度网络和显式的位置特征在卷积神经网络中，以将解剖结构位置信息与医学图像分析相整合，提高自动分割的准确性，实验结果表明，加入位置信息的网络与手工特征或不加入位置信息的网络相比较，取得了更好的结果。