On the promise that if human users know the cause of an output, it would
enable them to grasp the process responsible for the output, and hence provide
understanding, many explainable methods have been proposed to indicate the
cause for the output of a model based on its input. Nonetheless, little has
been reported on quantitative measurements of such causal relationships between
the inputs, the explanations, and the outputs of a model, leaving the
assessment to the user, independent of his level of expertise in the subject.
To address this situation, we explore a technique for measuring the causal
relationship between the features from the area of the object of interest in
the images of a class and the output of a classifier. Our experiments indicate
improvement in the causal relationships measured when the area of the object of
interest per class is indicated by a mask from an explainable method than when
it is indicated by human annotators. Hence the chosen name of Causal
Explanation Score (CaES)

基于可解释方法，通过定量测量图像分类中目标区域特征与输出之间的因果关系，我们提出了一种称为 Causal Explanation Score (CaES) 的方法，并通过实验证明了使用可解释方法中的掩模来指示目标区域的效果优于使用人工标注者。

因果评分的医学图像解释：基于离体肾结石图像的案例研究

Causal Scoring Medical Image Explanations: A Case Study On Ex-vivo  Kidney Stone Images

We have designed, developed, and applied a convolutional neural network (CNN)
architecture using multi-task learning to search for and characterize strong HI
Lya absorption in quasar spectra. Without any explicit modeling of the quasar
continuum nor application of the predicted line-profile for Lya from quantum
mechanics, our algorithm predicts the presence of strong HI absorption and
estimates the corresponding redshift zabs and HI column density NHI, with
emphasis on damped Lya systems (DLAs, absorbers with log NHI > 20.3). We tuned
the CNN model using a custom training set of DLAs injected into DLA-free quasar
spectra from the Sloan Digital Sky Survey (SDSS), data release 5 (DR5). Testing
on a held-back validation set demonstrates a high incidence of DLAs recovered
by the algorithm (97.4% as DLAs and 99% as an HI absorber with log NHI > 19.5)
and excellent estimates for zabs and NHI. Similar results are obtained against
a human-generated survey of the SDSS DR5 dataset. The algorithm yields a low
incidence of false positives and negatives but is challenged by overlapping
DLAs and/or very high NHI systems. We have applied this CNN model to the quasar
spectra of SDSS-DR7 and the Baryonic Oscillation Spectroscopic Survey (BOSS,
data release 12) and provide catalogs of 4,913 and 50,969 DLAs respectively
(including 1,659 and 9,230 high-confidence DLAs that were previously
unpublished). This work validates the application of deep learning techniques
to astronomical spectra for both classification and quantitative measurements.

本篇论文使用多目标学习的卷积神经网络来搜寻和表征夸克星光谱中的强氢 Lyman-Alpha 吸收，并完成分类和定量测量。该算法能够准确地预测 HI 吸收的存在性，并估计相应的 redshift zabs 和 HI 柱密度 NHI，并使用自定义的训练集进行模型训练，最后在 SDSS-DR7 和 BOSS 数据集中应用，证明了深度学习在天文光谱测量方面应用的有效性。