Keras implementation of wiseDNN for brain disease prognosis
The code was written by Dr. Mingxia Liu and Dr. Jun Zhang, Department of Radiology at UNC-CH.
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Introduction
We propose a weakly-supervised Densely-connected Neural Network (wiseDNN) for brain disease prognosis using baseline MRI data and incomplete clinical scores. Specifically, we first extract multi-scale image patches (located by anatomical landmarks) from structural MRI to capture local-to-global structural information of images, and then develop a weakly-supervised densely-connected network for task-oriented extraction of imaging features and joint prediction of multiple clinical measures. A weighted loss function is further employed to make full use of all available subjects (even those without ground-truth scores at certain time-points) for network training.
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Prerequisites
Linux python 2.7
Keras version 2.0.8
NVIDIA GPU + CUDA CuDNN (CPU mode, untested) Cuda version 8.0.61
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Installation
Install Keras and dependencies
Install numpywith pip install numpy
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Files
a. Source Code: Main.py, Generator.py, Loss.py, and Model.py
b. Data: img.npy, landmark.npy
c. Pre-trained Model: https://drive.google.com/file/d/1vJtDULrxEZqvxHcRiCOFzi-KrsOhKxDf/view?usp=sharing
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Implementation Detail
Copy the model to the folder of Model/
cd to folder Code/ and
Apply our Pre-trained Model with GPU
python Main.py
*Note we use the Keras backend as follows { "image_data_format": "channels_first", "floatx": "float32", "backend": "tensorflow" }