Support vector machines in DSC‐based glioma imaging: Suggestions for optimal characterization

By Frank G. Zöllner, Kyrre Eeg Emblem, Lothar Rudi Schad. 2010

37 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Neurology, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jakob Nikolas Kather
2. A. Marx
3. Cleo-Aron Weis
4. T. Gaiser
5. Susanne Melchers
6. Francesco Bianconi
7. Lothar Rudi Schad
8. Frank G. Zöllner

In Radiology, Nuclear Medicine and Imaging

1. Francesco Bianconi

Citations in the corpus

• Multi-class texture analysis in colorectal cancer histology (2016, methodology): “Typically, these methods extract texture features first 8,12–14 , then feed the features into a classifier to predict the tissue type 9,15,16 .”
• Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests (2011, background): “More recently, research has been steadily building on the accuracy and efficiency of data mining, with classifiers like Neural Networks (NN), Support Vector Machines (SVM), Classification Trees (CT) and Random Forests (RF) used for medical prediction and classification tasks [13,14,19-27].”