There are 3 repositories under monai topic.
AI Toolkit for Healthcare Imaging
Implementations of recent research prototypes/demonstrations using MONAI.
MONAI Generative Models makes it easy to train, evaluate, and deploy generative models and related applications
MONAI Label is an intelligent open source image labeling and learning tool.
Segmentation deep learning ALgorithm based on MONai toolbox: single and multi-label segmentation software developed by QIMP team-Vienna.
MONAI Deploy aims to become the de-facto standard for developing, packaging, testing, deploying and running medical AI applications in clinical production.
MONAI Deploy App SDK offers a framework and associated tools to design, develop and verify AI-driven applications in the healthcare imaging domain.
Developing a UNet3D model for accurate MRI skull stripping using the Calgary Campinas 359 dataset, enhancing neuroimaging preprocessing workflows.
A 3D Slicer extension to use AMASSS, ALI-CBCT and ALI-IOS
Repository to train Latent Diffusion Models on Chest X-ray data (MIMIC-CXR) using MONAI Generative Models
Code for the paper published in Deep Generative Models for Health Workshop at the Neurips 2023.
This is Pooya Mohammadi, Open Source Enthusiast, AI Developer & Researcher
cardiAc ultrasound Segmentation & Color-dopplEr dealiasiNg Toolbox (ASCENT)
Lighter is a framework for streamlining deep learning experiments with configuration files. PyTorch Lightning + MONAI Bundle.
An open source library for streaming and preprocessing point-of-care ultrasound video.
teeth segmentation using pytorch and monai
Building detection from the SpaceNet dataset using UNet.
A deep learning model and training/testing/inference library in PyTorch for segmentation, classification, object detection and self-supervised learning using radiology data.
A General Medical Image Segmentation Framework.(Multi-Modal, Mono-Modal, 2D, 3D)
All the code used in our YouTube videos (starting from 2024 videos) can be found here.
using labeled and unlabeled (and doing the labeling manually) data, the data is basically medical files (NIfTI & DICOM images) to ensure a good segmentation of the liver
Deep learning based cardiac segmentation
MONAI Label client plugin for napari
Applying CNNs, Decoders, and Transfer Learning to distinguish the MRIs of heavy cannabis users vs. controls
Brain white matter hyperintensity segmentation, with T1 and FLAIR MRI images, using UNet.
MRI-based semi-automated detection of ACL(Anterior Cruciate Ligament) injury using PyTorch
Brain Tumor Segmentation Pipeline for BraTS Challenge
Empowering 3D Lung Tumour Segmentation with MONAI
training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
Abdominal Trauma Detection Model using MONAI.