Dear all,
Our method FEM (Feature Explanation Method) for sample-based explanations of DNN décisions in Image Classification tasks has been released in OpenSource
You can find it here
https://github.com/labribkb/fem <https://github.com/labribkb/fem>
The summary description of it available in Open source is in here : Alexey Zhukov, Jenny Benois-Pineau, Romain Giot, «Evaluation of Explanation Methods of AI - CNNs in Image
Classification Tasks with Reference-based and No-reference Metrics », in Advances in Artificial Intelligence and Machine Learning; Research 2 (4) 620-646, Article in Publishing
https://www.oajaiml.com/uploads/archivepdf/20401143.pdf <https://www.oajaiml.com/uploads/archivepdf/20401143.pdf>
Its application to medical images is in the chapter n6 of the newly published Book Explainable Deep Learning - AI: Methods and Challenges, Elsevier , 2023
https://www.elsevier.com/books/explainable-deep-learning-ai/benois-pineau/978-0-323-96098-4 <https://www.elsevier.com/books/explainable-deep-learning-ai/benois-pineau/978-0-323-96098-4>;
The methods works without gradient computation but by a statistical analysis.
It is easy to play with, we thank you in advance for your feed-back.
Jenny Benois-Pineau
on behalf of the authors.
Jenny Benois-Pineau,
Professeure en Informatique,
Chargée de mission aux relations Internationales
Collège Sciences et Technologies,
Université de Bordeaux
351, crs de la Libération
33405 Talence
France
tel.: +33 (0) 5 40 00 84 24
Jenny Benois-Pineau, PhD, HDR,
Professor of Computer Science,
Chair of International relations
School of Sciences and Technologies
University of Bordeaux
351, crs de la Libération
33405 Talence
tel.: +33 (0) 5 40 00 84 24
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