机器学习工具包用于医疗图像分析 – DLTK

Deep Learning Toolkit (DLTK)

DLTK is a neural networks toolkit written in python, on top of Tensorflow
. Its modular architecture is closely inspired by sonnet
and it was developed to enable fast prototyping and ensure reproducibility in image analysis applications, with a particular focus on medical imaging. Its goal is to provide the community with state of the art methods and models and to accelerate research in this exciting field.

Documentation

DLTK API and user guides can be found here

Installation

  1. Install CUDA with cuDNN and add the path to ~/.bashrc:
LD_LIBRARY_PATH=$LD_LIBRARY_PATH:MY_CUDA_PATH/lib64; export LD_LIBRARY_PATH
LD_LIBRARY_PATH=$LD_LIBRARY_PATH:MY_CUDA_PATHextras/CUPTI/lib64; export LD_LIBRARY_PATH
PATH=$PATH:MY_CUDA_PATH/bin; export PATH
CUDA_HOME=MY_CUDA_PATH; export CUDA_HOME
  1. Setup a virtual environment and activate it:
virtualenv venv_tf1.1
source venv_tf1.1/bin/activate
  1. Install all DLTK dependencies (including tensorflow) via pip:
cd $DLTK_SRC
pip install -e .

Start playing

  1. Start a notebook server with
jupyter notebook --ip=* --port $MY_PORT
  1. navigate to examples and run a tutorial.ipynb notebook

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