Hippocampus

30 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Model-Free Episodic Control

ShibiHe/Model-Free-Episodic-Control 14 Jun 2016

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 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

dscarmo/e2dhipseg 12 Feb 2019

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 13 Jun 2019

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

dscarmo/e2dhipseg 14 Jan 2020

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.

Clique topology reveals intrinsic geometric structure in neural correlations

nebneuron/clique-top 22 Feb 2015

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

ehosseiniasl/3d-convolutional-network 2 Jul 2016

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

ehosseiniasl/3d-convolutional-network 2 Jul 2016

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

sgarg87/neurogenesis_inspired_dictionary_learning 22 Jan 2017

In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture.

Machine learning for neural decoding

KordingLab/Neural_Decoding 2 Aug 2017

Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods.