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

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

Publishing farabio==0.0.2:
PyPI | Release notes

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

Publishing farabio==0.0.1:
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This work is selected for PyTorch Ecosystem Day.

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

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

• biodatasets.ChestXrayDataset:
  https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
• biodatasets.DSB18Dataset:
  https://www.kaggle.com/c/data-science-bowl-2018/overview
• biodatasets.HistocancerDataset:
  https://www.kaggle.com/c/histopathologic-cancer-detection/overview
• biodatasets.RANZCRDataset:
  https://www.kaggle.com/c/ranzcr-clip-catheter-line-classification/overview
• biodatasets.RetinopathyDataset:
  https://www.kaggle.com/c/aptos2019-blindness-detection/overview
• biodatasets.VinBigDataset:
  https://www.kaggle.com/c/vinbigdata-chest-xray-abnormalities-detection
• biodatasets.MosmedDataset:
  https://www.kaggle.com/datasets/andrewmvd/mosmed-covid19-ct-scans
• biodatasets.EpiSeizureDataset: https://www.kaggle.com/datasets/harunshimanto/epileptic-seizure-recognition

• 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

• 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

• ConvNet3D - https://www.sciencedirect.com/science/article/pii/S0010482521001001

• VanillaLSTM - https://www.sciencedirect.com/science/article/abs/pii/S001048251830132X

conda create -n myenv python=3.8
conda activate myenv

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

A. With pip:

pip install farabio

B. Setup from source:

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

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
Tutorial 4: Training a 3D-CNN to predict the presence of viral pneumonia in computer tomography (CT) scans for MosmedDataset - Script
Tutorial 5: Training a LSTM for epileptic seizures prediction using EpiSeizureDataset dataset - Script

• 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

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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}
}

This work is licensed Apache 2.0.

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.