In this project we aim to implement and compare different RNN implementaion including LSTM, GRU and vanilla RNN for the task of time series binary classification. We also further visualize gate activities in different implementation to have a better understanding of the underlying signals.
Data could be any time-series data with binary label
Reults and methods are presented in detailed at [1]: (https://ieeexplore.ieee.org/abstract/document/8395313/)
Using Python Keras library (Keras 2.x) with [Tensorflow] backend: (https://www.tensorflow.org/versions/r0.7/tutorials/recurrent/index.html#recurrent-neural-networks)
[1] Azizi, Shekoofeh, et al. "Deep Recurrent Neural Networks for Prostate Cancer Detection: Analysis of Temporal Enhanced Ultrasound." IEEE transactions on medical imaging (2018).
If you are using these codes in any capicity please cite the above paper or:
@article{azizi2018deep, title={Deep Recurrent Neural Networks for Prostate Cancer Detection: Analysis of Temporal Enhanced Ultrasound}, author={Azizi, Shekoofeh and Bayat, Sharareh and Yan, Pingkun and Tahmasebi, Amir and Kwak, Jin Tae and Xu, Sheng and Turkbey, Baris and Choyke, Peter and Pinto, Peter and Wood, Bradford and others}, journal={IEEE transactions on medical imaging}, year={2018}, publisher={IEEE} }