Difference between revisions of "Monai"

Latest revision as of 17:46, 5 May 2023

Description

The MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm.

Project MONAI also includes MONAI Label, an intelligent open source image labeling and learning tool that helps researchers and clinicians collaborate, create annotated datasets, and build AI models in a standardized MONAI paradigm.

Environment Modules

Run module spider monai to find out what environment modules are available for this application.

System Variables

HPC_MONAI_DIR - installation directory
HPC_MONAI_BIN - executable directory
HPC_MONAI_LIB - library directory

HPC_MONAILABEL_DIR - MONAI label installation directory

More Information

For quick guides on usage for MONAI Core/MONAI Label, visit Monai Usage.

MONAI on HiPerGator Tutorial Recordings and Slides

Recording: MONAI Label for Medical Imaging and slides: MONAICore_tutorial_UF_July_2022.pdf

Recording: MONAI Core

GitHub repository with code examples: https://github.com/hw-ju/monai_uf_tutorials

MONAI 3-day Bootcamp Recordings and Learning Materials

Bootcamp Playlist

MONAI YouTube

MONAI Stream

MONAI Deploy App SDK

Spleen Segmentation Overview

Spleen Segmentation Deepdive

MedNIST Classification

MONAI Packages

If you would like to install MONAI on your own platforms, here are some useful links:

MONAI Core

MONAI Label

MONAI Deploy App SDK

MONAI Core tutorials

Tutorials

@@ Line 1: / Line 1: @@
-[[Category:Software]][[Category:Data Science in Healthcare]]
+[[Category:Software]][[Category:Healthcare]]
 {|<!--CONFIGURATION: REQUIRED-->
 |{{#vardefine:app|monai}}
@@ Line 32: / Line 32: @@
 * HPC_{{uc:{{#var:app}}}}LABEL_DIR - MONAI label installation directory
-==MONAI core==
+==More Information==
-          NGC container usage:
+'''For quick guides on usage for MONAI Core/MONAI Label, visit [[Monai Usage]].'''
-             ml purge
+=== MONAI on HiPerGator Tutorial Recordings and Slides ===
-             ml ngc-monai/<version>
+* Recording: [https://ufl.zoom.us/rec/play/2O1PqqMU7DJ2mexyn_Jfki3IGGcu3KjLV8iZ2dcT2Wztxx27dIO8g7W_nlWfEnKmoXASRFNBm7ihGWZU.n-AJRZLetesP4X9v?startTime=1658851332000 MONAI Label for Medical Imaging] and slides: [https://github.com/hw-ju/monai_uf_tutorials/blob/main/slides/MONAICore_tutorial_UF_July_2022.pdf MONAICore_tutorial_UF_July_2022.pdf]
-             python <your__python_script>
-==MONAI label==
+* Recording: [https://ufl.zoom.us/rec/share/4VXkVx0gpN1lrLFpmzOKd3CHqsuKFrok8FaJ2aBorm4KLEPFhWlLkBtHP-IQE_2S.5FNwh3n6D-cCTUJj?startTime=1657641745000 MONAI Core]
-          NGC container usage:
-) Server:
-                 To start the server as a slurm job:
-                    ml purge
-                    ml ngc-monailabel/<version>
-                    sbatch start_monai_server_readonly.sh
-                 Note server address from job output: used in next step
-) 3DSlicer client
-                - Start Open On Demand (OOD) session
-                - Start Console in hwgui with 1 GPU: gpu:geforce:1
-                - In console: load & start Slicer
-                     ml qt/5.15.4 slicer/4.13.0
-                     vglrun -d :0.$CUDA_VISIBLE_DEVICES Slicer
-                - In Slicer GUI:
-                     Select module: Active Learning -> MONAILabel
-                     Fill in server address, e.g.: http://c1007a-s17:8000/
-                     Click on refresh button next to server address
-                     Load Next Sample
-                You are good to go! Enjoy!
-==Learning Materials==
-=== MONAI on HiPerGator Tutorial Recordings and Slides ===
-* Recording: [https://ufl.zoom.us/rec/share/uqMac511BGJOAJRRwUK0n_vSKEJfYq4D8XFIO_jpqzMsC71GAYouwpAwKlNhQP-T.IEC0pYOfbYjTi_8W?startTime=1645549358000 MONAI Label for Medical Imaging] and slides: [[File:2022-Feb-MONAILabel-Tutorial-UF.pdf]], by Dr. Huiwen Ju, Feb. 22, 2022
-* Recording: [https://ufl.zoom.us/rec/share/BdCDL5iWtaRSSdBSLHqvjUUJyz6iAGuUVDeiN9VyvH7P9S-oJG7-YwfeOkh0M_8L.5BfMuChSdRbsoQHK?startTime=1646154153000 MONAI Core] and slides: [[File:2022-March-MONAICore-tutorial-UF.pdf]], by Dr. Huiwen Ju, March 1, 2022
+* GitHub repository with code examples: [https://github.com/hw-ju/monai_uf_tutorials https://github.com/hw-ju/monai_uf_tutorials]
 === MONAI 3-day Bootcamp Recordings and Learning Materials ===