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radtorch's Introduction

radtorch_1 0_header

DOI

What is RADTorch ?

RADTorch provides a framework of higher level classes and functions that significantly reduce the time needed for implementation of different machine and deep learning algorithms on DICOM and non-DICOM medical images.

RADTorch was built by radiologists for radiologists so they can build, test and implement state-of-the-art machine learning algorithms in minutes.

RADTorch is :

  • User-Friendly: Minimal coding required.

  • Comprehensive: Includes data analysis and visualization.

  • Backed by state-of-the-art machine learning and data visualization frameworks.

image

Install RADTorch

Stable version

To install stable version via pip, use the following command:

pip3 install git+https://download.radtorch.com/

Preview version To install Preview beta version via pip, use the following command:

pip3 install git+https://preview.radtorch.com/

Requests

Feature requests are more than welcomed on our discussion board HERE


Contribute

Bug reports and pull requests are welcome.


Cite us

@software{mohamed_elbanan_2022_6464087,
  author       = {Mohamed Elbanan},
  title        = {radtorch/radtorch: 1.0.0},
  month        = apr,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {1.0.0},
  doi          = {10.5281/zenodo.6464087},
  url          = {https://doi.org/10.5281/zenodo.6464087}
}

radtorch's People

Contributors

elbanan avatar kareemelfatairy avatar

Stargazers

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Watchers

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radtorch's Issues

Inference/CM/ROC takes long time

proposed solutions:
1- move items during model.eval() to GPU.
2- use dataloader with batch instead of items from dataset for inference.

Feature Requests

This ticket serves as a common place for feature requests.

Please feel free to comment below with desired features.

OSError in DataLoader worker process 0

Error

OSError in DataLoader worker process 0

Scenario

Working with image classification pipeline, when setting model_arch to resnet architecture this happens.

Cause

Error with dataloader num_workers , default=4. Needs to be set again to Zero

Version

0.1.2-beta

Solution

clf = pipeline.Image_Classification()
clf.train_data_loader.num_workers = 0
clf.valid_data_loader.num_workers = 0
clf.test_data_loader.num_workers = 0

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