Deciphering Machine Learning Decisions to Distinguish between Posterior Fossa Tumor Types Using MRI Features: What Do the Data Tell Us?

SIMPLE SUMMARY: This paper focuses on interpreting machine learning (ML) models’ decisions in medical diagnoses, specifically for four types of posterior fossa tumors in pediatric patients. The proposed methodology involves using kernel density estimations with Gaussian distributions to analyze indi...

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Detalles Bibliográficos
Autores principales: Tanyel, Toygar, Nadarajan, Chandran, Duc, Nguyen Minh, Keserci, Bilgin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10452543/
https://www.ncbi.nlm.nih.gov/pubmed/37627043
http://dx.doi.org/10.3390/cancers15164015

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10452543/
https://www.ncbi.nlm.nih.gov/pubmed/37627043
http://dx.doi.org/10.3390/cancers15164015

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