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awsome_deep_geometry_learning

A list of resources about deep learning solutions on 3D shape processing

boundary-loss

Official code for "Boundary loss for highly unbalanced segmentation", runner-up for best paper award at MIDL 2019. Extended version in MedIA, volume 67, January 2021.

brain-mri-autoencoder

Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brain

brain-tumor-classification

Brain Tumor Classification

brain-tumor-mri-dataset

Utilities to download and load an MRI brain tumor dataset with Python, providing 2D slices, tumor masks and tumor classes.

brain-tumor-segmentation

Pytorch implementaion of UNet, Deep ResUnet and ONet models for the brain tumor segmentation task

brain-tumor-segmentation-1

Brain Tumor Segmentation done using U-Net Architecture.

brain-tumour-segmentation-on-fmri-data-using-u-nets.

Built an MRI data processing module and standardized the voxel data, trained the model by taking random sub-samples from the 3D image and applied the U-net model to segment tumor regions in 3D brain MRI image. • Implemented a custom loss function for model training (soft dice loss) and evaluated model performance by calculating sensitivity and specificity.

brain2020

Development and validation of an interpretable deep learning framework for Alzheimer's disease classification

brain_tumor_grading

Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment

braintumorretrieval

brats-2020

A complete pipeline for BraTS 2020

brats2018_nvnet

Implementation of NvNet

caae

code for Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders

cam

Class Activation Mapping

ccd

Code for 'Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images' (MICCAI 2021)

chexnet

A pytorch reimplementation of CheXNet

chexnet-1

Implementation of the CheXNet network (PyTorch)

chexnet-with-localization

Weakly Supervised Learning for Findings Detection in Medical Images

cifar10-tensorflow

cifar10数据集上进行图片分类，基于tensorflow框架，旨在探究不同的改进策略对分类准确率的影响，如何一步步得提高准确率

clog-loss-advance-alzheimer-s-research

company_liver_kidney_seg

MR下肝脏和肾脏(左、右)的分割，给公司做的，pytorch

confidnet

Addressing Failure Prediction by Learning Model Confidence

connected-components-3d

Connected components on multilabel 3D & 2D images. Handles 26, 18, and 6 connected variants.

convolutional_autoencoder

Code for a convolutional autoencoder written on python, theano, lasagne, nolearn

cople-net

COVID-19 Pneumonia Lesion segmentation network

covid-19-detection

Detecting Covid-19 from X-ray

covid-chestxray-dataset

We are building an open database of COVID-19 cases with chest X-ray or CT images.

covid-net

COVID-Net Open Source Initiative

covidprognosis

COVID deterioration prediction based on chest X-ray radiographs via MoCo-trained image representations