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Hippocampus

Papers

Showing 125 of 300 papers

TitleStatusHype
HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language ModelsCode7
AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scansCode2
Conditional diffusion model with spatial attention and latent embedding for medical image segmentationCode1
CISCA and CytoDArk0: a Cell Instance Segmentation and Classification method for histo(patho)logical image Analyses and a new, open, Nissl-stained dataset for brain cytoarchitecture studiesCode1
Yucca: A Deep Learning Framework For Medical Image AnalysisCode1
Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise NetworksCode1
ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolutionCode1
NICE: Neurogenesis Inspired Contextual Encoding for Replay-free Class Incremental LearningCode1
MDD-UNet: Domain Adaptation for Medical Image Segmentation with Theoretical Guarantees, a Proof of ConceptCode1
M3D-NCA: Robust 3D Segmentation with Built-in Quality ControlCode1
Memory Encoding ModelCode1
Deep Reinforcement Learning with Task-Adaptive Retrieval via HypernetworkCode1
NAISR: A 3D Neural Additive Model for Interpretable Shape RepresentationCode1
Med-NCA: Robust and Lightweight Segmentation with Neural Cellular AutomataCode1
Continual Learning, Fast and SlowCode1
Label-Efficient Online Continual Object Detection in Streaming VideoCode1
3DConvCaps: 3DUnet with Convolutional Capsule Encoder for Medical Image SegmentationCode1
Continual Hippocampus Segmentation with TransformersCode1
3D-UCaps: 3D Capsules Unet for Volumetric Image SegmentationCode1
ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRICode1
DualNet: Continual Learning, Fast and SlowCode1
Stable deep neural network architectures for mitochondria segmentation on electron microscopy volumesCode1
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAECode1
A biologically plausible neural network for Slow Feature AnalysisCode1
Point process models for sequence detection in high-dimensional neural spike trainsCode1
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