Ting Luo's repositories
Covid-19-Detection
Detecting Covid-19 from X-ray
H-DenseUNet
TMI 2018. H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
tensorwatch
Debugging, monitoring and visualization for Python Machine Learning and Data Science
ANTs
Advanced Normalization Tools (ANTs)
DeScarGAN
Official Pytorch implementation of the paper DeScarGAN
BraTS2018_NvNet
Implementation of NvNet
GeodisTK
geodesic distance transform of 2d/3d images
COPLE-Net
COVID-19 Pneumonia Lesion segmentation network
Brain-Tumor-Segmentation-1
Brain Tumor Segmentation done using U-Net Architecture.
PlotNeuralNet
Latex code for making neural networks diagrams
AI-for-Medicine-Specialization-deeplearning.ai
My assignment soultions to the AI for Medicine Specialization course from coursera
awesome-anomaly-detection-in-medical-images
Awesome anomaly detection in medical images
BraTS-2020
A complete pipeline for BraTS 2020
SOTA-MedSeg
SOTA medical image segmentation methods based on various challenges
3D-Medical-Imaging-Preprocessing-All-you-need
This Repo Will contain the Preprocessing Code for 3D Medical Imaging
brain-tumor-mri-dataset
Utilities to download and load an MRI brain tumor dataset with Python, providing 2D slices, tumor masks and tumor classes.
Augmentor3D
A module for 3D image augmentations for deep learning, specifically medical images such as CT, MRI.
np_obb
Calculate oriented bounding boxes for label images in python
MONAI
AI Toolkit for Healthcare Imaging
cs-mri-gan
Structure preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networks (CVPRW 2020)
Improved-Body-Parts
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation
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.
PARIETAL
PARIETAL: Yet another deeP leARnIng brain ExTrAtion tooL
Gcam
Gcam is an easy to use Pytorch library that makes model predictions more interpretable for humans. It allows the generation of attention maps with multiple methods like Guided Backpropagation, Grad-Cam, Guided Grad-Cam and Grad-Cam++.
Attention-Gated-Networks
Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation