Updated README

README.md

@@ -9,10 +9,7 @@ Overview
 [![Docs](https://github.com/MMIV-ML/fastMONAI/actions/workflows/deploy.yaml/badge.svg)](https://fastmonai.no)
 [![PyPI](https://img.shields.io/pypi/v/fastMONAI?color=blue&label=PyPI%20version&logo=python&logoColor=white.png)](https://pypi.org/project/fastMONAI)
 
-A low-code Python-based open source deep learning library built on top
-of [fastai](https://github.com/fastai/fastai),
-[MONAI](https://monai.io/), and
-[TorchIO](https://torchio.readthedocs.io/).
+A low-code Python-based open source deep learning library built on top of [fastai](https://github.com/fastai/fastai), [MONAI](https://monai.io/), [TorchIO](https://torchio.readthedocs.io/), and [Imagedata](https://imagedata.readthedocs.io/).
 
 fastMONAI simplifies the use of state-of-the-art deep learning
 techniques in 3D medical image analysis for solving classification,
@@ -38,7 +35,7 @@ If you want to install an editable version of fastMONAI run:
 
 # Getting started
 
-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.
+The best way to get started using fastMONAI is to read the [paper](https://www.sciencedirect.com/science/article/pii/S2665963823001203) and 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                                                                                                                                                                                   |
 |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
@@ -50,4 +47,21 @@ The best way to get started using fastMONAI is to dive into our beginner-friendl
 # How to contribute
 
 See
-[CONTRIBUTING.md](https://github.com/MMIV-ML/fastMONAI/blob/master/CONTRIBUTING.md)
+[CONTRIBUTING.md](https://github.com/MMIV-ML/fastMONAI/blob/master/CONTRIBUTING.md)
+
+# Citing fastMONAI
+
+If you are using fastMONAI in your research, please use the following citation:
+
+@article{KALIYUGARASAN2023100583,
+title = {fastMONAI: A low-code deep learning library for medical image analysis},
+journal = {Software Impacts},
+pages = {100583},
+year = {2023},
+issn = {2665-9638},
+doi = {https://doi.org/10.1016/j.simpa.2023.100583},
+url = {https://www.sciencedirect.com/science/article/pii/S2665963823001203},
+author = {Satheshkumar Kaliyugarasan and Alexander S. Lundervold},
+keywords = {Deep learning, Medical imaging, Radiology},
+abstract = {We introduce fastMONAI, an open-source Python-based deep learning library for 3D medical imaging. Drawing upon the strengths of fastai, MONAI, and TorchIO, fastMONAI simplifies the use of advanced techniques for tasks like classification, regression, and segmentation. The library's design addresses domain-specific demands while promoting best practices, facilitating efficient model development. It offers newcomers an easier entry into the field while keeping the option to make advanced, lower-level customizations if needed. This paper describes the library's design, impact, limitations, and plans for future work.}
+}

fastMONAI/__init__.py

@@ -1 +1 @@
-__version__ = "0.4.0.1"
+__version__ = "0.4.0.2"

nbs/index.ipynb

@@ -26,7 +26,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A low-code Python-based open source deep learning library built on top of [fastai](https://github.com/fastai/fastai), [MONAI](https://monai.io/), and [TorchIO](https://torchio.readthedocs.io/). \n",
+    "A low-code Python-based open source deep learning library built on top of [fastai](https://github.com/fastai/fastai), [MONAI](https://monai.io/), [TorchIO](https://torchio.readthedocs.io/), and [Imagedata](https://imagedata.readthedocs.io/).\n",
     "\n",
     "fastMONAI simplifies the use of state-of-the-art deep learning techniques in 3D medical image analysis for solving classification, regression, and segmentation tasks. fastMONAI provides the users with functionalities to step through data loading, preprocessing, training, and result interpretations."
    ]
@@ -86,7 +86,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "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."
+    "The best way to get started using fastMONAI is to read the [paper](https://www.sciencedirect.com/science/article/pii/S2665963823001203) and 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."
    ]
   },
   {
@@ -114,6 +114,33 @@
    "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": [
+    "If you are using fastMONAI in your research, please use the following citation:\n",
+    "\n",
+    "@article{KALIYUGARASAN2023100583,\n",
+    "title = {fastMONAI: A low-code deep learning library for medical image analysis},\n",
+    "journal = {Software Impacts},\n",
+    "pages = {100583},\n",
+    "year = {2023},\n",
+    "issn = {2665-9638},\n",
+    "doi = {https://doi.org/10.1016/j.simpa.2023.100583},\n",
+    "url = {https://www.sciencedirect.com/science/article/pii/S2665963823001203},\n",
+    "author = {Satheshkumar Kaliyugarasan and Alexander S. Lundervold},\n",
+    "keywords = {Deep learning, Medical imaging, Radiology},\n",
+    "abstract = {We introduce fastMONAI, an open-source Python-based deep learning library for 3D medical imaging. Drawing upon the strengths of fastai, MONAI, and TorchIO, fastMONAI simplifies the use of advanced techniques for tasks like classification, regression, and segmentation. The library's design addresses domain-specific demands while promoting best practices, facilitating efficient model development. It offers newcomers an easier entry into the field while keeping the option to make advanced, lower-level customizations if needed. This paper describes the library's design, impact, limitations, and plans for future work.}\n",
+    "}"
+   ]
   }
  ],
  "metadata": {

settings.ini

@@ -5,7 +5,7 @@
 ### Python Library ###
 lib_name = fastMONAI
 min_python = 3.7
-version = 0.4.0.1
+version = 0.4.0.2
 ### OPTIONAL ###
 
 requirements = fastai==2.7.12 monai==1.2.0 torchio==0.18.91 xlrd>=1.2.0 scikit-image==0.19.3 imagedata==2.1.3 huggingface-hub gdown gradio opencv-python