DLTK 用于医学图像分析的深度学习工具箱-优快云博客

DLTK是一款基于TensorFlow的神经网络工具包，采用模块化设计，受到Sonnet的启发。它专注于图像分析应用，特别是医学成像领域，旨在促进快速原型设计并确保研究的可重复性。

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Deep Learning Toolkit (DLTK)

DLTK is a neural networks toolkit written in python, on top ofTensorflow. Its modular architecture is closely inspired bysonnetand 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 foundhere

Installation

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

Setup a virtual environment and activate it:

virtualenv venv_tf1.1
source venv_tf1.1/bin/activate

Install all DLTK dependencies (including tensorflow) via pip:

cd $DLTK_SRC
pip install -e .

Start playing

Start a notebook server with

jupyter notebook --ip=* --port $MY_PORT

navigate to examples and run a tutorial.ipynb notebook

Road map

Over the course of the next months we will add more content to DLTK. This road map outlines the immediate plans for what you will be seeing in DLTK soon:

Core:
- Losses: Dice loss, frequency reweighted losses, adversial training
- Normalisation: layer norm, weight norm
Models:
- deepmedic
- densenet
- VGG
- Super-resolution nets
Other:
- Augmentation via elastic deformations
- Sampling with fixed class frequencies
- Stand-alone deploy scripts