Towards complementary explanations using deep neural networks

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Wilson Silva (Author) Other; Kelwin Fernandes (Author) Other; Maria J. Cardoso (Author) Other; Jaime S. Cardoso (Author) FEUP

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 133-140

1st International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, 1st International Workshop on Deep Learning Fails, DLF 2018, and 1st International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018

16 September 2018 through 20 September 2018

Indexing

Scopus - 10 Citations

INSPEC

Other information

Authenticus ID: P-00P-VVA

DOI: 10.1007/978-3-030-02628-8_15

Abstract (EN): Interpretability is a fundamental property for the acceptance of machine learning models in highly regulated areas. Recently, deep neural networks gained the attention of the scientific community due to their high accuracy in vast classification problems. However, they are still seen as black-box models where it is hard to understand the reasons for the labels that they generate. This paper proposes a deep model with monotonic constraints that generates complementary explanations for its decisions both in terms of style and depth. Furthermore, an objective framework for the evaluation of the explanations is presented. Our method is tested on two biomedical datasets and demonstrates an improvement in relation to traditional models in terms of quality of the explanations generated. © Springer Nature Switzerland AG 2018.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8