🤖 farabio ❤️

• What's New
• Introduction
• Features
• Biodatasets
• Models
• Getting Started (Installation)
• Tutorials
• Links
• Credits
• Licenses
• Acknowledgements

🎉 What's New

August 26, 2021

Publishing farabio==0.0.3 (latest version):
PyPI | Release notes

August 18, 2021

Publishing farabio==0.0.2:
PyPI | Release notes

April 21, 2021

This work is presented at PyTorch Ecosystem day. Poster is here.

April 2, 2021

Publishing farabio==0.0.1:
PyPI | Release notes

March 3, 2021

This work is selected for PyTorch Ecosystem Day.

💡 Introduction

farabio is a minimal PyTorch toolkit for out-of-the-box deep learning support in biomedical imaging. For further information, see Wikis and Docs.

🔥 Features

• Biomedical datasets
• Common DL models
• Flexible trainers (*in progress)

📚 Biodatasets

• Chest X-Ray Images (Pneumonia):
  https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
• 2018 Data Science Bowl:
  https://www.kaggle.com/c/data-science-bowl-2018/overview
• Histopathologic Cancer Detection:
  https://www.kaggle.com/c/histopathologic-cancer-detection/overview
• RANZCR CLiP - Catheter and Line Position Challenge:
  https://www.kaggle.com/c/ranzcr-clip-catheter-line-classification/overview
• APTOS 2019 Blindness Detection:
  https://www.kaggle.com/c/aptos2019-blindness-detection/overview

🚢 Models

Classification:

• AlexNet - https://papers.nips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
• GoogLeNet - https://arxiv.org/pdf/1409.4842.pdf
• MobileNetV2 - https://arxiv.org/pdf/1801.04381.pdf
• MobileNetV3 - https://arxiv.org/pdf/1905.02244.pdf
• ResNet - https://arxiv.org/pdf/1512.03385.pdf
• ShuffleNetV2 - https://arxiv.org/pdf/1807.11164.pdf
• SqueezeNet - https://arxiv.org/pdf/1602.07360.pdf
• VGG - https://arxiv.org/pdf/1409.1556.pdf

Segmentation:

• DeepLabV3 - https://arxiv.org/pdf/1706.05587
• U-Net - https://arxiv.org/pdf/1505.04597
• LinkNet - https://arxiv.org/pdf/1707.03718
• PSPNet - https://arxiv.org/pdf/1612.01105
• FPN - http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf

🚀 Getting started (Installation)

1. Create and activate conda environment:

conda create -n myenv python=3.8
conda activate myenv

2. Install PyTorch:

pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

3. Install farabio:

A. With pip:

pip install farabio

B. Setup from source:

git clone https://github.com/tuttelikz/farabio.git && cd farabio
pip install .

🤿 Tutorials

Tutorial 1: Training a classifier for ChestXrayDataset - Notebook
Tutorial 2: Training a segmentation model for DSB18Dataset - Notebook
Tutorial 3: Training a Faster-RCNN detection model for VinBigDataset - Notebook

🔎 Links

• API documentations: https://farabio.readthedocs.io
• Code: https://github.com/tuttelikz/farabio
• Issue tracker: https://github.com/tuttelikz/farabio/issues
• PyPI package: https://pypi.org/project/farabio/
• Wiki: https://github.com/tuttelikz/farabio/wiki

⭐ Credits

If you like this repository, please click on Star.

How to cite | doi:

@software{sanzhar_askaruly_2021_5746474,
  author       = {Sanzhar Askaruly and
                  Nurbolat Aimakov and
                  Alisher Iskakov and
                  Hyewon Cho and
                  Yujin Ahn and
                  Myeong Hoon Choi and
                  Hyunmo Yang and
                  Woonggyu Jung},
  title        = {Farabio: Deep learning for biomedical imaging},
  month        = dec,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v0.0.3-doi},
  doi          = {10.5281/zenodo.5746474},
  url          = {https://doi.org/10.5281/zenodo.5746474}
}

📃 Licenses

This work is licensed Apache 2.0.

🤩 Acknowledgements

This work is based upon efforts of open-source PyTorch Community. I have tried to acknowledge related works (github links, arxiv papers) inside the source material, eg. README, documentation, and code docstrings. Please contact if I missed anything.