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Explainable Deep Learning Methodologies for Biomedical Images Classification

Conference Paper
Publication Date:
2022
abstract:
Often when we have a lot of data available we can not give them an interpretability and an explainability such as to be able to extract answers, and even more so diagnosis in the medical field. The aim of this contribution is to introduce a way to provide explainability to data and features that could escape even medical doctors, and that with the use of Machine Learning models can be categorized and "explained".
Iris type:
04.01 Contributo in Atti di convegno
Keywords:
classification; Deep Learning model; explainability; robustness
List of contributors:
Martinelli, Fabio
Authors of the University:
MARTINELLI FABIO
Handle:
https://iris.cnr.it/handle/20.500.14243/418265
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http://www.scopus.com/record/display.url?eid=2-s2.0-85140875536&origin=inward
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