… issues of brain tumor, glioma, classification from four … convolutional graph-based label
propagation framework, based on the … function of label propagation for semi-supervised learning. …

… Finally, to show how different subtypes are in terms of biological functions, we propose a
network propagation method with permutation-based normalization for identification of …

… in tumor WSIs were removed from subtype classification by automatic ROI delineation, and
the features utilized for subtype classification can be updated by back propagation during …

… and precise classification of cancer. There is high importance in the cancer’s treatment for
classifying tumor correctly. Due to … Supervised models have been utilized for the purpose of …

… and classification is the convolutional neural network (CNN). We … tumor classification of
three tumor types. The developed network is simpler than already-existing pre-trained networks, …

… One of the potential applications could be using those features for supervised classification,
… training the connected network with forward and backward propagation between the original …

… 309 strong image augmentations for label propagation. The basic 310 assumption of semi-…
For the classification backbone 442 this work utilized the state of the art image classification …

… In this study, we present a comprehensive benchmarking of supervised learning for
network-based gene classification, evaluating this approach and a classic label propagation …

… ), a supervised approach to perform classification tasks with a … transferred to the back
propagation stage of the error signal. … In diabetes determination and cancer determination, our …

… how a neural network makes a classification decision. … The neural network learns to classify
the type of cancer from two 2D … back-propagated and corrected to identify accurately tumor …