Skip to content

Commit

Permalink
Doc: updated documentation
Browse files Browse the repository at this point in the history
  • Loading branch information
skaliy committed Sep 4, 2023
1 parent c6c6a82 commit 00f46f6
Show file tree
Hide file tree
Showing 17 changed files with 5 additions and 1,982 deletions.
17 changes: 2 additions & 15 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -38,12 +38,7 @@ If you want to install an editable version of fastMONAI run:

# Getting started

The best way to get started using fastMONAI is to read the
[paper](https://github.com/MMIV-ML/fastMONAI/tree/master/paper) and look
at the step-by-step tutorial-like notebooks to learn how to train your
own models on different tasks (e.g., classification, regression,
segmentation). See the docs at https://fastmonai.no for more
information.
The best way to get started using fastMONAI is to dive into our beginner-friendly [video](https://fastmonai.no/tutorial_beginner_video). For a deeper understanding and hands-on experience, our comprehensive instructional notebooks will walk you through model training for various tasks like classification, regression, and segmentation. See the docs at https://fastmonai.no for more information.

| Notebook | 1-Click Notebook |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
Expand All @@ -55,12 +50,4 @@ information.
# How to contribute

See
[CONTRIBUTING.md](https://github.com/MMIV-ML/fastMONAI/blob/master/CONTRIBUTING.md)

# Citing fastMONAI

@article{kaliyugarasan2022fastMONAI,
title={fastMONAI: a low-code deep learning library for medical image analysis},
author={Kaliyugarasan, Satheshkumar and Lundervold, Alexander Selvikv{\aa}g},
year={2022}
}
[CONTRIBUTING.md](https://github.com/MMIV-ML/fastMONAI/blob/master/CONTRIBUTING.md)
2 changes: 1 addition & 1 deletion fastMONAI/__init__.py
Original file line number Diff line number Diff line change
@@ -1 +1 @@
__version__ = "0.4.0"
__version__ = "0.4.1"
22 changes: 1 addition & 21 deletions nbs/index.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -86,7 +86,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The best way to get started using fastMONAI is to read the [paper](https://github.com/MMIV-ML/fastMONAI/tree/master/paper) and look at the step-by-step tutorial-like notebooks to learn how to train your own models on different tasks (e.g., classification, regression, segmentation). See the docs at https://fastmonai.no for more information. "
"The best way to get started using fastMONAI is to dive into our beginner-friendly [video](https://fastmonai.no/tutorial_beginner_video). For a deeper understanding and hands-on experience, our comprehensive instructional notebooks will walk you through model training for various tasks like classification, regression, and segmentation. See the docs at https://fastmonai.no for more information."
]
},
{
Expand Down Expand Up @@ -114,26 +114,6 @@
"source": [
"See [CONTRIBUTING.md](https://github.com/MMIV-ML/fastMONAI/blob/master/CONTRIBUTING.md)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Citing fastMONAI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"@article{kaliyugarasan2022fastMONAI,\n",
" title={fastMONAI: a low-code deep learning library for medical image analysis},\n",
" author={Kaliyugarasan, Satheshkumar and Lundervold, Alexander Selvikv{\\aa}g},\n",
" year={2022}\n",
"}\n",
"```"
]
}
],
"metadata": {
Expand Down
3 changes: 0 additions & 3 deletions paper/README.md

This file was deleted.

130 changes: 0 additions & 130 deletions paper/paper.bib

This file was deleted.

Loading

0 comments on commit 00f46f6

Please sign in to comment.