MONAI: An open-source framework for deep learning in healthcare

Abstract

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

arXiv commentwww.monai.io

Cited by in corpus (28)

• Segment Anything Model for Medical Images?
• Pseudo-CTs from T1-weighted MRI for planning of low-intensity transcranial focused ultrasound neuromodulation: an open-source tool
• medigan: a Python library of pretrained generative models for medical image synthesis
• GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
• Towards contrast-agnostic soft segmentation of the spinal cord
• Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review
• Open and reusable deep learning for pathology with WSInfer and QuPath
• Federated brain tumor segmentation: an extensive benchmark
• A Deep Learning-Based Fully Automated Pipeline for Regurgitant Mitral Valve Anatomy Analysis From 3D Echocardiography
• Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2
• A Cascade Transformer-based Model for 3D Dose Distribution Prediction in Head and Neck Cancer Radiotherapy
• Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection
• SLOctolyzer: Fully automatic analysis toolkit for segmentation and feature extracting in scanning laser ophthalmoscopy images
• Precision-medicine-toolbox: An open-source python package for facilitation of quantitative medical imaging and radiomics analysis
• Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation
• Classification of Multi-Parametric Body MRI Series Using Deep Learning
• 3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs
• A voxel-level approach to brain age prediction: A method to assess regional brain aging
• landmarker: a Toolkit for Anatomical Landmark Localization in 2D/3D Images
• Predicting Age from White Matter Diffusivity with Residual Learning
• A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers
• Multi-temporal crack segmentation in concrete structures using deep learning approaches
• Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge
• A solution for co-locating 2D histology images in 3D for histology-to-CT and MR image registration: closing the loop for bone sarcoma treatment planning
• Architecture Analysis and Benchmarking of 3D U-shaped Deep Learning Models for Thoracic Anatomical Segmentation
• StoDIP: Efficient 3D MRF image reconstruction with deep image priors and stochastic iterations
• Subclass Classification of Gliomas Using MRI Fusion Technique
• Fit Pixels, Get Labels: Meta-learned Implicit Networks for Image Segmentation