Hippocampus
39 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Hippocampus
Most implemented papers
Model-Free Episodic Control
State of the art deep reinforcement learning algorithms take many millions of interactions to attain human-level performance.
Continual Learning with Deep Generative Replay
kuc2477/pytorch-deep-generative-replay
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NeurIPS 2017
Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting.
Extended 2D Consensus Hippocampus Segmentation
Segmentation done by experts is considered to be a gold-standard when evaluating automated methods, buts it is a time consuming and arduos task, requiring specialized personnel.
Enforcing temporal consistency in Deep Learning segmentation of brain MR images
bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets
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Proposed CNN based segmentation approaches demonstrate how 2D segmentation using prior slices can provide similar results to 3D segmentation while maintaining good continuity in the 3D dimension and improved speed.
Hippocampus Segmentation on Epilepsy and Alzheimer's Disease Studies with Multiple Convolutional Neural Networks
We test this methodology alongside other recent deep learning methods, in two domains: The HarP test set and an in-house epilepsy dataset, containing hippocampus resections, named HCUnicamp.
3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation
Medical image segmentation has been so far achieving promising results with Convolutional Neural Networks (CNNs).
Clique topology reveals intrinsic geometric structure in neural correlations
Detecting meaningful structure in neural activity and connectivity data is challenging in the presence of hidden nonlinearities, where traditional eigenvalue-based methods may be misleading.
Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network
The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans.
Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network
The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans.
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World
In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture.
