PyTorch toolkit for biomedical imaging

Related tags

Pytorch Utilitiespythondeep-learningmodelspytorchdatasetsbiomedicalbiomedical-imaging
Overview

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🤖 farabio ❤️

PyPI version DOI PyPI - Downloads Documentation Status GitHub commit activity GitHub

🎉 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

🚢 Models

Classification:

Segmentation:

🚀 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

⭐ 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.

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Comments
  • invalid input type

    invalid input type

    Instructions To Reproduce the Bug

    1. What exact command you run:
    If making changes to the project itself, please use output of the following command:
git rev-parse HEAD; git diff

<put code or diff here>
    1. Full logs or other relevant observations:
    <put logs here>
    1. please simplify the steps as much as possible so they do not require additional resources to run, such as a private dataset.

    Expected behavior:

    If there are no obvious error in "what you observed" provided above, please tell us the expected behavior.

    Environment:

    Provide your environment information using the following command:

    git clone https://gist.github.com/tuttelikz/ebd5ab3ffb29cb9399f2596b8f163a4e a && python a/cenv.py
    opened by aminemosbah 3
Releases(v0.0.3-doi)

  • v0.0.3-doi(Dec 1, 2021)

  • v0.0.3(Aug 25, 2021)

  • v0.0.2(Aug 17, 2021)

    TLDR: This is a fresh, restructured release package compared to v0.0.1. Here, we ship several classification models and biodatasets in PyTorch friendly format.

    Models:

    • AlexNet
    • GoogLeNet
    • MobileNetV2
    • MobileNetV3
    • ResNet
    • ShuffleNetV2
    • SqueezeNet
    • VGG

    Biodatasets:

    • ChestXrayDataset
    • DSB18Dataset
    • HistocancerDataset
    • RANZCRDataset
    • RetinopathyDataset
    Source code(tar.gz)
    Source code(zip)
    farabio-0.0.2-py3-none-any.whl(32.98 KB)

  • v0.0.1(Aug 25, 2021)

    TLDR: This is the very first release. In this release, we ship various baseline models for classification, segmentation, detection, super-resolution and image translation tasks. As well, basis for model trainers and biodatasets are described here. Architectures are not as clean. Please refer to new releases in the future.

    Biodatasets:

    • ChestXrayDataset
    • DSB18Dataset
    • HistocancerDataset
    • RANZCRDataset
    • RetinopathyDataset

    Trainers:

    • BaseTrainer
    • ConvnetTrainer
    • GanTrainer

    Models:

    • DenseNet
    • GoogLeNet
    • VGG
    • ResNet
    • MobileNetV2
    • ShuffleNetV2
    • ViT
    • U-Net
    • Attention U-Net
    • FasterRCNN
    • YOLOv3
    • CycleGAN
    • SRGAN
    Source code(tar.gz)
    Source code(zip)
    farabio-0.0.1-py3-none-any.whl(100.73 KB)
Owner
San Askaruly
Willing to join fast-paced team to build amazing future!
San Askaruly
GitHub Repository https://farabio.readthedocs.io
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