josedolz
Associate Professor at the ETS, in Montreal. Interested in machine/deep learning methods for medical image interpretation.
ETS MontrealMontreal, Canada
Pinned Repositories
3D-F-CNN-BrainStruct
This repository contains materials employed in our work to segment subcortical brain structures by employing a 3D fully Convolutional Neural Network.
fewshot-segmentation
On the Texture Bias for Few-Shot CNN Segmentation
HyperDenseNet
This repository contains the code of HyperDenseNet, a hyper-densely connected CNN to segment medical images in multi-modal image scenarios.
HyperDenseNet_pytorch
Pytorch version of the HyperDenseNet deep neural network for multi-modal image segmentation
IVD-Net
Repository containing the source code of the IVD-Net segmentation network that we proposed for the MICCAI 2018 IVD segmentation challenge.
LiviaNET
This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"
LiviaNet_pytorch
This repository contains the pytorch implementation of our LiviaNET architecture
MSL-student-becomes-master
SemiDenseNet
Repository containing the code of one of the networks that we employed in the iSEG Grand MICCAI Challenge 2017, infant brain segmentation.
UnbiasedShapeCompactness
This repository contains the code employed in our work: "Unbiased Shape Compactness for segmentation"
josedolz's Repositories
josedolz/LiviaNET
This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"
josedolz/HyperDenseNet
This repository contains the code of HyperDenseNet, a hyper-densely connected CNN to segment medical images in multi-modal image scenarios.
josedolz/HyperDenseNet_pytorch
Pytorch version of the HyperDenseNet deep neural network for multi-modal image segmentation
josedolz/SemiDenseNet
Repository containing the code of one of the networks that we employed in the iSEG Grand MICCAI Challenge 2017, infant brain segmentation.
josedolz/IVD-Net
Repository containing the source code of the IVD-Net segmentation network that we proposed for the MICCAI 2018 IVD segmentation challenge.
josedolz/MSL-student-becomes-master
josedolz/LiviaNet_pytorch
This repository contains the pytorch implementation of our LiviaNET architecture
josedolz/UnbiasedShapeCompactness
This repository contains the code employed in our work: "Unbiased Shape Compactness for segmentation"
josedolz/3D-F-CNN-BrainStruct
This repository contains materials employed in our work to segment subcortical brain structures by employing a 3D fully Convolutional Neural Network.
josedolz/fewshot-segmentation
On the Texture Bias for Few-Shot CNN Segmentation
josedolz/cvml-reading_group
Reading group for CV and ML.
josedolz/FSMS-Surrogate
Semi-supervised few-shot learning for medical image segmentation
josedolz/Progressive_Dilated_UNet
josedolz/RePRI-for-Few-Shot-Segmentation
(CVPR 2021) Code for our method RePRI for Few-Shot Segmentation. Paper at http://arxiv.org/abs/2012.06166
josedolz/academic-kickstart
Easily create a beautiful website using Academic and Hugo
josedolz/academic-kickstart-netlify-cms
This is an integration of academic theme with Netlify CMS
josedolz/caffe-release_segmentation
josedolz/chainer
A flexible framework of neural networks for deep learning
josedolz/CRac
josedolz/CSrankings
A web app for ranking computer science departments according to their research output in selective venues.
josedolz/fewshot-segmentation-1
josedolz/Folder-Structure-Conventions
Folder / directory structure options and naming conventions for software projects
josedolz/google-research
Google Research
josedolz/josedolz.github.io
josedolz/keras-vis
Neural network visualization toolkit for keras
josedolz/markdown-cheatsheet
Markdown Cheatsheet for Github Readme.md
josedolz/PFENet
PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation (TPAMI).
josedolz/Realistic-Neural-Talking-Head-Models
My implementation of Few-Shot Adversarial Learning of Realistic Neural Talking Head Models (Egor Zakharov et al.).