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Found 5 papers, 5 papers with code
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders.
Exploring Intensity Invariance in Deep Neural Networks for Brain Image Registration
We find a performance degradation of these models when brain image pairs with different intensity distribution are presented even with similar structures.
Decoding Neural Responses in Mouse Visual Cortex through a Deep Neural Network
Our results indicate that neurons in mouse visual cortex encode the features of natural and artificial objects in a distinct manner, and such neural code is consistent across animals.
Developing Brain Atlas through Deep Learning
Neuroscientists have devoted significant effort into the creation of standard brain reference atlases for high-throughput registration of anatomical regions of interest.
Exploring Brain-wide Development of Inhibition through Deep Learning
We introduce here a fully automated convolutional neural network-based method for brain image processing to Detect Neurons in different brain Regions during Development (DeNeRD).
