Metadata-Version: 2.1
Name: SAMITorch
Version: 0.2.7
Summary: Deep Learning Framework For Medical Image Analysis
Home-page: UNKNOWN
Author: Benoit Anctil-Robitaille and Pierre-Luc Delisle
Author-email: benoit.anctil-robitaille.1@ens.etsmtl.ca
License: MIT
Description: # <img src="/icons/artificial-intelligence.png" width="60" vertical-align="bottom"> SAMITorch
        
        ## Welcome to SAMITorch
        
        [![Build Status](https://travis-ci.com/sami-ets/SAMITorch.svg?branch=master)](https://travis-ci.com/sami-ets/SAMITorch)
        ![GitHub All Releases](https://img.shields.io/github/downloads/sami-ets/SAMITorch/total.svg)
        ![GitHub issues](https://img.shields.io/github/issues/sami-ets/SAMITorch.svg)
        ![GitHub](https://img.shields.io/github/license/sami-ets/SAMITorch.svg)
        ![GitHub contributors](https://img.shields.io/github/contributors/sami-ets/SAMITorch.svg)
        
        
        SAMITorch is a deep learning framework for *Shape Analysis in Medical Imaging* laboratory of [École de technologie supérieure](https://www.etsmtl.ca/) using [PyTorch](https://github.com/pytorch) library.
        It implements an extensive set of loaders, transformers, models and data sets suited for deep learning in medical imaging.
        Our objective is to build a tested, standard framework for quickly producing results in deep learning reasearch applied to medical imaging. 
        
        # Table Of Contents
        
        -  [Authors](#authors)
        -  [References](#references)
        -  [Project architecture](#project-architecture)
            -  [Folder structure](#folder-structure)
            -  [Main Components](#main-components)
                -  [Models](#models)
                -  [Transformers](#transformers)
                -  [Configuration](#configs)
                -  [Main](#main)
         -  [Contributing](#contributing)
         -  [Branch naming](#branch-naming)
         -  [Commits syntax](#commits-syntax)
         -  [Acknowledgments](#acknowledgments)
         
         
        ## Authors
        
        * Pierre-Luc Delisle - [pldelisle](https://github.com/pldelisle) 
        * Benoit Anctil-Robitaille - [banctilrobitaille](https://github.com/banctilrobitaille)
        
        ## References
        
        #### Segmentation
        ```
        @article{RN10,
           author = {Çiçek, Özgün and Abdulkadir, Ahmed and Lienkamp, Soeren S. and Brox, Thomas and Ronneberger, Olaf},
           title = {3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation},
           journal = {eprint arXiv:1606.06650},
           pages = {arXiv:1606.06650},
           url = {https://ui.adsabs.harvard.edu/\#abs/2016arXiv160606650C},
           year = {2016},
           type = {Journal Article}
        }
        ```
        
        #### Classification
        ```
        @inproceedings{RN12,
           author = {He, K. and Zhang, X. and Ren, S. and Sun, J.},
           title = {Deep Residual Learning for Image Recognition},
           booktitle = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
           pages = {770-778},
           ISBN = {1063-6919},
           DOI = {10.1109/CVPR.2016.90},
           type = {Conference Proceedings}
        }
        ```
        
        #### Diffusion imaging
        
        #### Application
        
        
        ## Setup
        > pip install -r [path/to/requirements.txt]  
        > python3 <main_script>.py
        
        
        ## Project architecture
        ### Folder structure
        
        ```
        ── samitorch
        |    ├── configs                 - This folder contains the YAML configuration files.
        |    │   ├── configurations.py       - This file contains the definitions of different configuration classes.
        |    │   |── resnet3d.yaml           - Standard ResNet 3D configuration file and model definition.
        |    │   └── unet3d.yaml             - Standard UNet 3D configuration file and model definition.
        |    |
        |    ├── initializers            - This folder contains custom layer/op initializers.  
        |    |   └── initializers.py
        |    │
        |    ├── inputs                  - This folder contains anything relative to inputs to a network.
        |    |   |── batch.py                - Contains Batch definition object used in training. 
        |    |   |── datasets.py             - Contains basic dataset definition for classification and segmentation.
        |    |   |── images.py               - Contains Enums for various methods.
        |    |   |── patch.py                - Contains Patch definition used in segmentation problems.
        |    |   |── sample.py               - Contains a Sample object.
        |    |   |── transformers.py         - Contains a series of common transformations.
        |    |   └── utils.py                - Contains various utilitary methods.
        |    |   
        |    ├── models                  - This folder contains any standard and tested deep learning models.
        |    │   |── layers.py               - Contains layer definitions. 
        |    |   |── resnet3d.py             - Contains a standard ResNet 3D model.
        |    |   └── unet3d.py               - Contains a standard UNet 3D model.                   
        |    |
        |    |── parsers                 - This folder contains parsers definition used in SAMITorch.
        |    |
        |    ├── preprocessing           - This folder contains anything relative to input preprocessing, and scripts that must be executed prior training.
        |    |
        |    └── utils                   - This folder contains any utils you may need.
        |         |── files.py              - Contains file related utils methods.
        |         |── slice_builder.py      - Contains an object to build slices out of a data sets (for image segmentation).
        |         └── tensors.py            - Contains tensor related utils methods.            
        ── tests                   - Folder containing unit tests of the standard framework api and functions.
        
        ```
        
        ### Main components
        (To be documented shortly...)
        #### Models
        
        #### Transformers
        
        #### Configs
        
        #### Main
        
        ## Contributing
        If you find a bug or have an idea for an improvement, please first have a look at our [contribution guideline](https://github.com/sami-ets/SAMITorch/blob/master/CONTRIBUTING.md). Then,
        - [X] Create a branch by feature and/or bug fix
        - [X] Get the code
        - [X] Commit and push
        - [X] Create a pull request
        
        ## Branch naming
        
        | Instance        | Branch                                              | Description, Instructions, Notes                   |
        |-----------------|-----------------------------------------------------|----------------------------------------------------|
        | Stable          | stable                                              | Accepts merges from Development and Hotfixes       |
        | Development     | dev/ [Short description] [Issue number]             | Accepts merges from Features / Issues and Hotfixes |
        | Features/Issues | feature/ [Short feature description] [Issue number] | Always branch off HEAD or dev/                     |
        | Hotfix          | fix/ [Short feature description] [Issue number]     | Always branch off Stable                           |
        
        ## Commits syntax
        
        ##### Adding code:
        > \+ Added [Short Description] [Issue Number]
        
        ##### Deleting code:
        > \- Deleted [Short Description] [Issue Number]
        
        ##### Modifying code:
        > \* Changed [Short Description] [Issue Number]
        
        ##### Merging branches:
        > Y Merged [Short Description]
        
        ## To build documentation
        
        SAMITorch uses Sphinx Documentation. To build doc, simply execute the following: 
        
        > cd docs  
        > sphinx-build -b html source build  
        
        
        ## Acknowledgment
        Thanks to [École de technologie supérieure](https://www.etsmtl.ca/), [Hervé Lombaert](https://profs.etsmtl.ca/hlombaert/) and [Christian Desrosiers](https://www.etsmtl.ca/Professeurs/cdesrosiers/Accueil) for providing us a lab and helping us in our research activities.
        
        Icons made by <a href="http://www.flaticon.com/authors/freepik" title="Freepik">Freepik</a> from <a href="http://www.flaticon.com" title="Flaticon">www.flaticon.com</a> is licensed by <a href="http://creativecommons.org/licenses/by/3.0/" title="Creative Commons BY 3.0" target="_blank">CC 3.0 BY</a>
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
