There are 3 repositories under monai topic.
AI Toolkit for Healthcare Imaging
Implementations of recent research prototypes/demonstrations using MONAI.
MONAI Label is an intelligent open source image labeling and learning tool.
MONAI Generative Models makes it easy to train, evaluate, and deploy generative models and related applications
(NeurIPS 2022 CellSeg Challenge - 1st Winner) Open source code for "MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy"
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.
A 3D Slicer extension to use AMASSS, ALI-CBCT and ALI-IOS
Developing a UNet3D model for accurate MRI skull stripping using the Calgary Campinas 359 dataset, enhancing neuroimaging preprocessing workflows.
Repository to train Latent Diffusion Models on Chest X-ray data (MIMIC-CXR) using MONAI Generative Models
Streamline deep learning experiments using config files
Code for the paper published in Deep Generative Models for Health Workshop at the Neurips 2023.
cardiAc ultrasound Segmentation & Color-dopplEr dealiasiNg Toolbox (ASCENT)
This is Pooya Mohammadi, Open Source Enthusiast, AI Developer & Researcher
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.
All the code used in our YouTube videos (starting from 2024 videos) can be found here.
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)
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
Empowering 3D Lung Tumour Segmentation with MONAI
Applying CNNs, Decoders, and Transfer Learning to distinguish the MRIs of heavy cannabis users vs. controls
training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
Brain white matter hyperintensity segmentation, with T1 and FLAIR MRI images, using UNet.
Code for the paper CTFSH: Full Head CT Anomaly Detection with Unsupervised Learning
MRI-based semi-automated detection of ACL(Anterior Cruciate Ligament) injury using PyTorch
Brain Tumor Segmentation Pipeline for BraTS Challenge
Abdominal Trauma Detection Model using MONAI.