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Found 5 papers, 2 papers with code
Robust and Generalisable Segmentation of Subtle Epilepsy-causing Lesions: a Graph Convolutional Approach
On a multi-centre dataset of 1015 participants with surface-based features and manual lesion masks from structural MRI data, the proposed GCN achieved an AUC of 0. 74, a significant improvement against a previously used vertex-wise multi-layer perceptron (MLP) classifier (AUC 0. 64).
Noise transfer for unsupervised domain adaptation of retinal OCT images
Optical coherence tomography (OCT) imaging from different camera devices causes challenging domain shifts and can cause a severe drop in accuracy for machine learning models.
Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale
Here we present a new workflow for mapping cytoarchitectonic areas in large series of cell-body stained histological sections of human postmortem brains.
Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks
We show that the self-supervised model has implicitly learned to distinguish several cortical brain areas -- a strong indicator that the proposed auxiliary task is appropriate for cytoarchitectonic mapping.
Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks
Its high resolution allows the study of laminar and columnar patterns of cell distributions, which build an important basis for the simulation of cortical areas and networks.
