Maede Maftouni's repositories
binary_mask_from_json
Making binary mask images from JSON annotation
Curated_Covid_CT
We built a large lung CT scan dataset for COVID-19 by curating data from 7 different public datasets. These datasets have been publicly used in COVID-19 diagnosis literature and proven their efficiency in deep learning applications. Therefore, the merged dataset is expected to improve the generalization ability of deep learning methods by learning from all these resources together.
Awesome-Convolutional-Attention-Mechanism-papers
The most influential papers in convolutional attention mechanisms, suitable for image classification, image and video object segmentation and tracking
CovidGlobal
Code & model files for Rahmandad, Lim & Sterman (2020), Estimating the global spread of COVID-19
Patient_Aware_Splitter
It is important not to split images of the same patient between the test and train sets to avoid overfitting. This repository splits a sample Covid/Normal classification dataset into test and train sets in a patient aware and stratified manner.
Unet-Segmentation-Pytorch-Nest-of-Unets
Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
COVIDNet-CT
COVID-Net Open Source Initiative - Models for COVID-19 Detection from Chest CT
FEELVOS
FEELVOS implementation in PyTorch; FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation
GSCNN
Gated-Shape CNN for Semantic Segmentation (ICCV 2019)
image
PHP Image Manipulation
Image_Segmentation
Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.
indigo
:ramen: Minimalist Jekyll Template
maftouni.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Pandas_Jump_Start
Pandas is the library one should master for data wrangling and analysis, and to prepare data for machine learning pipeline. This repository is a jump start to Pandas and its most useful functions.
RANet
RANet: Ranking Attention Network for Fast Video Object Segmentation (VOS), ICCV2019
robot-surgery-segmentation
Wining solution and its improvement for MICCAI 2017 Robotic Instrument Segmentation Sub-Challenge
rvos
RVOS: End-to-End Recurrent Network for Video Object Segmentation (CVPR 2019)
TernausNet
UNet model with VGG11 encoder pre-trained on Kaggle Carvana dataset
Time_Series_Muti_Step_Ahead_Prediction
There is a safety concern about temperature spike during the cutting. Our goal is to predict the temperature spike height and location multiple steps ahead to prevent it by adjusting the parameters of the machine through a closed-loop control system.