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Not again! Data Leakage in Digital Pathology
We verify that accuracy scores may be inflated up to 41%, even if a well-designed 10x5 iterated cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits.
A multiobjective deep learning approach for predictive classification in Neuroblastoma
Neuroblastoma is a strongly heterogeneous cancer with very diverse clinical courses that may vary from spontaneous regression to fatal progression; an accurate patient's risk estimation at diagnosis is essential to design appropriate tumor treatment strategies.
Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer
Convolutional Neural Networks (CNNs) are a popular deep learning architecture widely applied in different domains, in particular in classifying over images, for which the concept of convolution with a filter comes naturally.
Phylogenetic Convolutional Neural Networks in Metagenomics
We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure.
