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.

PyTorch Extension Library of Optimized Scatter Operations

PyTorch Scatter Documentation This package consists of a small extension library of highly optimized sparse update (scatter and segment) operations fo

1.2k Jan 7, 2023

PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations

PyTorch Sparse This package consists of a small extension library of optimized sparse matrix operations with autograd support. This package currently

757 Jan 4, 2023

Reformer, the efficient Transformer, in Pytorch

Reformer, the Efficient Transformer, in Pytorch This is a Pytorch implementation of Reformer https://openreview.net/pdf?id=rkgNKkHtvB It includes LSH

1.8k Jan 6, 2023

higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.

higher is a library providing support for higher-order optimization, e.g. through unrolled first-order optimization loops, of "meta" aspects of these

1.5k Jan 3, 2023

PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf

README TabNet : Attentive Interpretable Tabular Learning This is a pyTorch implementation of Tabnet (Arik, S. O., & Pfister, T. (2019). TabNet: Attent

2k Dec 27, 2022

PyTorch extensions for fast R&D prototyping and Kaggle farming

Pytorch-toolbelt A pytorch-toolbelt is a Python library with a set of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming: What

1.3k Jan 5, 2023

An implementation of Performer, a linear attention-based transformer, in Pytorch

Performer - Pytorch An implementation of Performer, a linear attention-based transformer variant with a Fast Attention Via positive Orthogonal Random

900 Dec 22, 2022

The goal of this library is to generate more helpful exception messages for numpy/pytorch matrix algebra expressions.

Tensor Sensor See article Clarifying exceptions and visualizing tensor operations in deep learning code. One of the biggest challenges when writing co

704 Dec 14, 2022

You like pytorch? You like micrograd? You love tinygrad! ❤️

For something in between a pytorch and a karpathy/micrograd This may not be the best deep learning framework, but it is a deep learning framework. Due

9.7k Jan 5, 2023

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

A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).

Code release for "Bayesian Compression for Deep Learning" In "Bayesian Compression for Deep Learning" we adopt a Bayesian view for the compression of

190 Dec 30, 2022

Use Jax functions in Pytorch with DLPack

106 Dec 17, 2022

High-fidelity performance metrics for generative models in PyTorch

5 Oct 24, 2021

This is an differentiable pytorch implementation of SIFT patch descriptor.

This is an differentiable pytorch implementation of SIFT patch descriptor. It is very slow for describing one patch, but quite fast for batch. It can

150 Dec 24, 2022

Tez is a super-simple and lightweight Trainer for PyTorch. It also comes with many utils that you can use to tackle over 90% of deep learning projects in PyTorch.

Tez: a simple pytorch trainer NOTE: Currently, we are not accepting any pull requests! All PRs will be closed. If you want a feature or something does

1.1k Jan 04, 2023

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

You might also like...

PyTorch Extension Library of Optimized Scatter Operations

PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations

Reformer, the efficient Transformer, in Pytorch

higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.

PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf

PyTorch extensions for fast R&D prototyping and Kaggle farming

An implementation of Performer, a linear attention-based transformer, in Pytorch

The goal of this library is to generate more helpful exception messages for numpy/pytorch matrix algebra expressions.

You like pytorch? You like micrograd? You love tinygrad! ❤️

Comments

invalid input type

Instructions To Reproduce the Bug

Expected behavior:

Environment:

Releases(v0.0.3-doi)

v0.0.3-doi(Dec 1, 2021)

v0.0.3(Aug 25, 2021)

v0.0.2(Aug 17, 2021)

v0.0.1(Aug 25, 2021)

Owner

San Askaruly

A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).

Use Jax functions in Pytorch with DLPack

High-fidelity performance metrics for generative models in PyTorch

This is an differentiable pytorch implementation of SIFT patch descriptor.

Tez is a super-simple and lightweight Trainer for PyTorch. It also comes with many utils that you can use to tackle over 90% of deep learning projects in PyTorch.

A simplified framework and utilities for PyTorch

A pure Python implementation of Compact Bilinear Pooling and Count Sketch for PyTorch.

Over9000 optimizer

GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks

Reformer, the efficient Transformer, in Pytorch

Pytorch bindings for Fortran

Fast Discounted Cumulative Sums in PyTorch

Fast and Easy-to-use Distributed Graph Learning for PyTorch Geometric

Kaldi-compatible feature extraction with PyTorch, supporting CUDA, batch processing, chunk processing, and autograd

A tiny package to compare two neural networks in PyTorch

A Closer Look at Structured Pruning for Neural Network Compression

Bunch of optimizer implementations in PyTorch

Tacotron 2 - PyTorch implementation with faster-than-realtime inference

Riemannian Adaptive Optimization Methods with pytorch optim

Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)

1. Create and activate `conda` environment:

2. Install `PyTorch`:

3. Install `farabio`: