Name
Title | ||
|---|---|---|
A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases. |
Abstract | ||
|---|---|---|
Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests. |
Year | Venue | Field |
|---|---|---|
2016 | ESANN | Decision tree,AdaBoost,Pattern recognition,Computer science,Matrix decomposition,Non-negative matrix factorization,Knowledge extraction,Artificial intelligence,Linear discriminant analysis,Artificial neural network,Random forest,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Victor Mocioiu | 1 | 0 | 0.34 |
| Nuno Miguel Pedrosa de Barros | 2 | 0 | 0.34 |
| Sandra Ortega-Martorell | 3 | 0 | 1.01 |
| Johannes Slotboom | 4 | 12 | 2.40 |
| Urspeter Knecht | 5 | 0 | 1.69 |
| Carles Arus | 6 | 43 | 3.79 |
| Alfredo Vellido | 7 | 373 | 51.92 |
| Margarida Julià-Sapé | 8 | 0 | 0.34 |