farabio - Deep learning for biomedical imaging
Project description
farabio
- What's New
- Introduction
- Features
- Biodatasets
- Models
- Getting Started (Installation)
- Tutorials
- Links
- Credits
- Licenses
- Acknowledgements
What's New
August 18, 2021
Release of farabio==0.0.2
April 21, 2021
This work is presented at PyTorch Ecosystem day. Poster is here.
April 2, 2021
Release of farabio==0.0.1
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. 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
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.
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.
Project details
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