Cargando…
Explainable Artificial Intelligence for Bias Detection in COVID CT-Scan Classifiers
Problem: An application of Explainable Artificial Intelligence Methods for COVID CT-Scan classifiers is presented. Motivation: It is possible that classifiers are using spurious artifacts in dataset images to achieve high performances, and such explainable techniques can help identify this issue. Ai...
Autores principales: | Palatnik de Sousa, Iam, Vellasco, Marley M. B. R., Costa da Silva, Eduardo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8402377/ https://www.ncbi.nlm.nih.gov/pubmed/34451100 http://dx.doi.org/10.3390/s21165657 |
Ejemplares similares
-
Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases
por: Palatnik de Sousa, Iam, et al.
Publicado: (2019) -
Explainable Artificial Intelligence for COVID-19 Diagnosis Through Blood Test Variables
por: Thimoteo, Lucas M., et al.
Publicado: (2022) -
Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine
por: Gniadek, Thomas, et al.
Publicado: (2023) -
Explainability and Transparency of Classifiers for Air-Handling Unit Faults Using Explainable Artificial Intelligence (XAI)
por: Meas, Molika, et al.
Publicado: (2022) -
Convolutional ensembles for Arabic Handwritten Character and Digit Recognition
por: Palatnik de Sousa, Iam
Publicado: (2018)