Lab for Artificial Intelligence in Medical Imaging 's repositories
squeeze_and_excitation
PyTorch Implementation of 2D and 3D 'squeeze and excitation' blocks for Fully Convolutional Neural Networks
quickNAT_pytorch
PyTorch Implementation of QuickNAT and Bayesian QuickNAT, a fast brain MRI segmentation framework with segmentation Quality control using structure-wise uncertainty
StablePose
Official Pytorch Implementation of Paper - Stable-Pose: Leveraging Transformers for Pose-Guided Text-to-Image Generation - NeurIPS 2024
relaynet_pytorch
Pytorch Implementation of retinal OCT Layer Segmentation (with trained models)
QuickNATv2
Fast Whole Brain Segmentation (Layers, codes and Pre-trained Models)
nn-common-modules
Pytorch Implementations of Common modules, blocks and losses for CNNs specifically for segmentation models
causal-effects-in-alzheimers-continuum
Code for the paper "Identification of causal effects of neuroanatomy on cognitive decline requires modeling unobserved confounders"
Dataset-Bias
Detect and correct bias in neuroimaging
TripletTraining
Official PyTorch Implementation for From Barlow Twins to Triplet Training: Differentiating Dementia with Limited Data - MIDL 2024
mlv2-net
MLV^2-Net: Rater-Based Majority-Label Voting for Consistent Meningeal Lymphatic Vessel Segmentation
MetadataNorm
Repository for the paper "Metadata Normalization"
pcl-protopnet-nw
Our work aims to integrate interpretability of neural networks and self-supervised learning on unlabelled datasets.