ujjwalbaid0408 Goto Github PK

anonymize-slide

Delete the label from a whole-slide image

awesome-semantic-segmentation

:metal: awesome-semantic-segmentation

brain-tumor-segmentation-and-analysis

This will help researchers to find the important papers, databases etc. associated with BTS and analysis

brats-toolkit

Code to preprocess, segment, and fuse glioma MRI scans based on the BraTS Toolkit manuscript.

deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

deep-learning-notes

fetschallenge2022

The repo for the FeTS Challenge

fl-tutorial

This is for FL tutorial at MICCAI 2022

gandlf

A generalizable application framework for segmentation, regression, and classification using PyTorch

github-action-workshop

github-actions-workshop

github-actions-workshop_new

itcr2023_1

labelfusion

Implementation of various label fusion approaches for medical imaging.

medical-datasets

tracking medical datasets, with a focus on medical imaging

medperf

An open benchmarking platform for medical artificial intelligence using Federated Evaluation.

objectrecognition

Uses the iOS device camera and a custom deep learning algorithm to perform object recognition.

ottr_template_website

python-tutorial-with-examples

This tutorial will help to learn basics of Python Programming

pytorch-unet

Pytorch implementation of the U-Net for image semantic segmentation, with dense CRF post-processing

qubiq

radiomics

refuge2020

tensorflow-tutorials

TensorFlow Tutorials with YouTube Videos

tutorials

Here, we will be showcasing our seminar series “CPP for Image Processing and Machine Learning” including presentations and code examples. There are image processing and machine learning libraries out there which use C++ as a base and have become industry standards (ITK for medical imaging, OpenCV for computer vision and machine learning, Eigen for linear algebra, Shogun for machine learning). The documentation provided with these packages, though extensive, assume a certain level of experience with C++. Our tutorials are intended for those people who have basic understanding of medical image processing and machine learning but who are just starting to get their toes wet with C++ (and possibly have prior experience with Python or MATLAB). Here we will be focusing on how someone with a good theoretical background in image processing and machine learning can quickly prototype algorithms using CPP and extend them to create meaningful software packages.

tutorials-1

MONAI Tutorials

ujjwalbaid.github.io

Github Pages template for academic personal websites