论文信息 - ciu.image: An R Package for Explaining Image Classification with Contextual Importance and Utility

ciu.image: An R Package for Explaining Image Classification with Contextual Importance and Utility

Many techniques have been proposed in recent years that attempt to explain results of image classifiers, notably for the case when the classifier is a deep neural network. This paper presents an implementation of the Contextual Importance and Utility method for explaining image classifications. It is an R package that can be used with the most usual image classification models. The paper shows results for typical benchmark images, as well as for a medical data set of gastroenterological images. For comparison, results produced by the LIME method are included. Results show that CIU produces similar or better results than LIME with significantly shorter calculation times. However, the main purpose of this paper is to bring the existence of this package to general knowledge and use, rather than comparing with other explanation methods.

[1] Pascal Fua,et al. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2] Kary Främling,et al. Modélisation et apprentissage des préférences par réseaux de neurones pour l'aide à la décision multicritère. (Learning and Explaining Preferences with Neural Networks for Multiple Criteria Decision Making) , 1996 .

[3] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[4] Kary Främling,et al. Explaining Machine Learning-Based Classifications of In-Vivo Gastral Images , 2019, 2019 Digital Image Computing: Techniques and Applications (DICTA).

[5] Argentina Leite,et al. A Deep Learning Approach for Red Lesions Detection in Video Capsule Endoscopies , 2018, ICIAR.