Adaloglou Nikolas's repositories
MedicalZooPytorch
A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation
3D-GAN-pytorch
Responsible implementation of 3D-GAN NIPS 2016 paper:Learning a Probabilistic Latent Space of ObjectShapes via 3D Generative-Adversarial Modeling,that can be found https://papers.nips.cc/paper/6096-learning-a-probabilistic-latent-space-of-object-shapes-via-3d-generative-adversarial-modeling.pdf
ct-intensity-segmentation
Introduction to medical image processing with Python: CT lung and vessel segmentation without labels https://theaisummer.com/medical-image-python/
MICCAI-2019-Prostate-Cancer-segmentation-challenge
Medical Deep Learning 2D high resolution image segmentation project: MICCAI 2019 Prostate Cancer segmentation challenge
Unity_Mesh_Deform_and_Smooth
simple mesh deform and smooth in Unity
slurm-hpc-survival-guide
Notes on using the HPC system
medical_image_DTI_MRI
Medical Image processing project DTI & T1-MRI
Neural_Network_lib_CUDA
Fully connected Neural Network CUDA C Library. MEng Thesis implemented in 2016-2017 in pure C/CUDA 8.
normalized_graph_cuts
Demystifying spectral clustering with graph cuts for unsupervised image segmentation
ECG_signal_processing
ECG signal processing assignment in Matlab
dgm-eval
Codebase for evaluation of deep generative models as presented in Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
flow_semantic_segmentation
FlowNet 2.0 with an auxilary semantic segmentation loss
logic_gate_simulator_Java
Undergraduate Java project
medical_robotics_3d
Medical Robotics postgraduate project - Implemented in January 2018
NVAE
The Official PyTorch Implementation of "NVAE: A Deep Hierarchical Variational Autoencoder" (NeurIPS 2020 spotlight paper)
PyTorch-StudioGAN
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.