PyTorch toolkit for biomedical imaging

Last update: Dec 28, 2022

Overview

🤖 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.

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Comments

invalid input type
Instructions To Reproduce the Bug

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>

Full logs or other relevant observations:

<put logs here>

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)

TLDR: Zenodo integration
Source code(tar.gz)
Source code(zip)
v0.0.3(Aug 25, 2021)
TLDR: In this release, we ship segmentation models. In each of their architectures, backbone part is shared so that every segmentation model can be used with different backbone.

Architectures:

DeepLabV3

U-Net

LinkNet

PSPNet

FPN

Backbones:

VGG

ResNet

MobileNetV2

Source code(tar.gz)
Source code(zip)
farabio-0.0.3-py3-none-any.whl(44.85 KB)
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!

GitHub Repository https://farabio.readthedocs.io

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PyTorch toolkit for biomedical imaging

Related tags

Overview

🤖 farabio ❤️

🎉 What's New

August 26, 2021

August 18, 2021

April 21, 2021

April 2, 2021

March 3, 2021

💡 Introduction

🔥 Features

📚 Biodatasets

🚢 Models

Classification:

Segmentation:

🚀 Getting started (Installation)

1. Create and activate conda environment:

2. Install PyTorch:

3. Install farabio:

🤿 Tutorials

🔎 Links

⭐ Credits

📃 Licenses

🤩 Acknowledgements

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PyTorch toolkit for biomedical imaging

1. Create and activate `conda` environment:

2. Install `PyTorch`:

3. Install `farabio`: