%0 Conference Proceedings
%T Interpretability of a Deep Learning Model for Rodents Brain Semantic Segmentation
%+ Federal University of Sergipe = Université fédérale de Sergipe (UFS)
%+ Life and Health Sciences Research Institute [Braga] (ICVS)
%+ Centro Algoritmi [Guimarães]
%A Matos, Leonardo, Nogueira
%A Rodrigues, Mariana, Fontainhas
%A Magalhães, Ricardo
%A Alves, Victor
%A Novais, Paulo
%Z Part 7: Deep Learning - Convolutional ANN
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Hersonissos, Greece
%Y John MacIntyre
%Y Ilias Maglogiannis
%Y Lazaros Iliadis
%Y Elias Pimenidis
%I Springer International Publishing
%3 Artificial Intelligence Applications and Innovations
%V AICT-559
%P 307-318
%8 2019-05-24
%D 2019
%R 10.1007/978-3-030-19823-7_25
%K Deep Learning Model
%K Magnetic Resonance Imaging
%K Interpretability
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-02331345/document
%2 https://inria.hal.science/hal-02331345/file/483292_1_En_25_Chapter.pdf
%L hal-02331345
%U https://inria.hal.science/hal-02331345
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-559