Hippocampus
54 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Hippocampus
Most implemented papers
Continual Hippocampus Segmentation with Transformers
MECLabTUDA/Lifelong-nnUNet
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Our evaluation on hippocampus segmentation shows that Transformer mechanisms mitigate catastrophic forgetting for medical image segmentation compared to purely convolutional architectures, and demonstrates that regularising ViT modules should be done with caution.
Spike-based computational models of bio-inspired memories in the hippocampal CA3 region on SpiNNaker
The human brain is the most powerful and efficient machine in existence today, surpassing in many ways the capabilities of modern computers.
3DConvCaps: 3DUnet with Convolutional Capsule Encoder for Medical Image Segmentation
Capsule network is a recent new architecture that has achieved better robustness in part-whole representation learning by replacing pooling layers with dynamic routing and convolutional strides, which has shown potential results on popular tasks such as digit classification and object segmentation.
Hippocluster: an efficient, hippocampus-inspired algorithm for graph clustering
Interestingly, information processing in the brain may suggest a simpler method of learning clusters directly from random walks.
Label-Efficient Online Continual Object Detection in Streaming Video
showlab/efficient-cls
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ICCV 2023
Remarkably, with only 25% annotated video frames, our method still outperforms the base CL learners, which are trained with 100% annotations on all video frames.
Expanding continual few-shot learning benchmarks to include recognition of specific instances
cerenaut/cfsl
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Continual learning and few-shot learning are important frontiers in progress towards broader Machine Learning (ML) capabilities.
Continual Learning, Fast and Slow
phquang/DualNet
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Motivated by this theory, we propose \emph{DualNets} (for Dual Networks), a general continual learning framework comprising a fast learning system for supervised learning of pattern-separated representation from specific tasks and a slow learning system for representation learning of task-agnostic general representation via Self-Supervised Learning (SSL).
Structured Recognition for Generative Models with Explaining Away
changmin-yu/structured-recognition-neurips-2022
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A key goal of unsupervised learning is to go beyond density estimation and sample generation to reveal the structure inherent within observed data.
An automated, geometry-based method for hippocampal shape and thickness analysis
deep-mi/hipsta
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In this work, we propose an automated, geometry-based approach for the unfolding, point-wise correspondence, and local analysis of hippocampal shape features such as thickness and curvature.
NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
uncbiag/naisr
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However, given a set of 3D shapes with associated covariates there is at present no shape representation method which allows to precisely represent the shapes while capturing the individual dependencies on each covariate.
