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Found 5 papers, 3 papers with code
The Data Representativeness Criterion: Predicting the Performance of Supervised Classification Based on Data Set Similarity
In a broad range of fields it may be desirable to reuse a supervised classification algorithm and apply it to a new data set.
Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms
There has recently been great progress in automatic segmentation of medical images with deep learning algorithms.
Learning an MR acquisition-invariant representation using Siamese neural networks
Generalization of voxelwise classifiers is hampered by differences between MRI-scanners, e. g. different acquisition protocols and field strengths.
MR Acquisition-Invariant Representation Learning
Due to this acquisition related variation, classifiers trained on data from a specific scanner fail or under-perform when applied to data that was acquired differently.
Automatic segmentation of MR brain images with a convolutional neural network
Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages.
