fastMONAI
Low-code deep learning for 3D medical image analysis
All Skills in This Repository (7)
Pure Emerald Level Indicators
MMIV-MLstaff-review
Assess technical plans for risks and missing details with structured reviews.
MMIV-MLdocker-local-dev
Generate Docker-based development environments for PHP, Node.js, and Python stacks.
MMIV-MLskill-creator
Generate, package and validate Claude skill scaffolds with Python scripts and SKILL.md templates.
MMIV-MLcode-reviewer
Review Python code for quality, security, and performance issues.
MMIV-MLror-workflow
Explore DICOM datasets and set up ROR research workflows.
MMIV-MLfastmonai-upstream-guide
Consult MONAI, TorchIO, and nnU-Net docs and source code to align fastMONAI implementations with upstream patterns.
MMIV-MLclaude-docs-consultant
Fetch official Claude Code documentation on demand for API usage and feature references.
Frequently Asked Questions
FAQPage SchemaHow to install fastMONAI?▼
Run `npx skills add MMIV-ML/fastMONAI --all -g -y` in your terminal to install all skills in this suite globally.
What is fastMONAI used for?▼
It is a low-code Python library for 3D medical image analysis, covering classification, regression, and segmentation of MRI and CT scans.
Can I train medical imaging models without deep learning experience?▼
Yes. fastMONAI provides beginner-friendly notebooks and tutorials that handle data loading, preprocessing, training, and inference with minimal code.
Does fastMONAI support large 3D volumes that don't fit in memory?▼
Yes. It includes patch-based training with lazy loading and sliding-window inference so memory usage stays constant on large scans.
Do I need a GPU to use fastMONAI?▼
A CUDA-compatible GPU is recommended for training, but inference on new scans works on CPU.
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