Mariano Cabezas (marianocabezas)

marianocabezas

Geek Repo

Company:University of Sydney

Location:Sydney

Home Page:marianocabezas.github.io

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Mariano Cabezas's repositories

brainharmonic

BrainHarmonics project repository for the Brainhack Australasia 2021

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challenges2017

Repository with models, experiments and approaches for the BraTS 2017 and iSeg segmentation challenges.

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challenges2018

Repository for the MICCAI challenges of 2018

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quad22

Repository with approaches for super angular resolution on raw diffusion data

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diffusion_metrics

Temporary repository for the development of a set of unified functions for diffusion analysis (from FOD to connectome)

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fetal-brain

Repository with code for our work related to fetal brain ultrasound

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msseg2

Segmentation pipelines fo rthe MSSEG2 MICCAI challenge

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braintools

Repository with C++ code based on ITK for brain MRI processing

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data_manipulation

CNN tests with pytorch and other tools

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Challenge

The repo for the FeTS Challenge

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cnn-keras

CNN repository based on Keras

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cnn-longitudinal

Repository with a CNN implementation for logitudinal MS lesion segmentation.

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miccai_challenge2016

Files for the MSSEG challenge of the MICCAI 2016. This repository containes code and the weights for the two nets.

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cnn-nolearn

Repository with CNN code base on Lasagne and nolearn

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differentiable-plasticity

Implementations of the algorithms described in Differentiable plasticity: training plastic networks with gradient descent, a research paper from Uber AI Labs.

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hearing

Repository to analyse hearing impairment data

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marianocabezas.github.io

Personal webpage

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pytorch-code

Repository to work on pyTorch

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rethinking_dsc

Project to test some toy examples on the DSC loss for highly unbalanced datasets

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SlidesMD

Repository that contains slides based on markdown

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synthetic_3d_data

Synthetic dataset experiments for image classification networks

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tensorflow_tests

Just tensorflow tests

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voxelmorph

Unsupervised Learning for Image Registration

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wetlands

Repository with code to test different ideas for a review paper on deep learning for wetlands

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