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The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Interpretability of a Deep Learning Model for Rodents Brain Semantic Segmentation

Authors : Leonardo Nogueira Matos, Mariana Fontainhas Rodrigues, Ricardo Magalhães, Victor Alves, Paulo Novais

Published in: Artificial Intelligence Applications and Innovations

Publisher: Springer International Publishing

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Abstract

In recent years, as machine learning research has become real products and applications, some of which are critical, it is recognized that it is necessary to look for other model evaluation mechanisms. The commonly used main metrics such as accuracy or F-statistics are no longer sufficient in the deployment phase. This fostered the emergence of methods for interpretability of models. In this work, we discuss an approach to improving the prediction of a model by interpreting what has been learned and using that knowledge in a second phase. As a case study we have used the semantic segmentation of rodent brain tissue in Magnetic Resonance Imaging. By analogy with what happens to the human visual system, the experiment performed provides a way to make more in-depth conclusions about a scene by carefully observing what attracts more attention after a first glance in en passant.

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Metadata
Title
Interpretability of a Deep Learning Model for Rodents Brain Semantic Segmentation
Authors
Leonardo Nogueira Matos
Mariana Fontainhas Rodrigues
Ricardo Magalhães
Victor Alves
Paulo Novais
Copyright Year
2019
Book
Artificial Intelligence Applications and Innovations

Print ISBN: 978-3-030-19822-0

Electronic ISBN: 978-3-030-19823-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-19823-7_25

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