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README.md

SD-Layer

This repository contains the code accompanying our MICCAI 2017 paper "SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging". The following figure (borrowed from our paper) illustrates the architecture of SD-Layer.

Fig 1. An illustration of SD-Layer.

For more details, please refer to our paper or poster.

Citing this code

If you find this code useful in your research, please consider citing:

    Duggal R., Gupta A., Gupta R., Mallick P. (2017) SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging.
    In: Descoteaux M., Maier-Hein L., Franz A., Jannin P., Collins D., Duchesne S. (eds) Medical Image Computing and Computer-Assisted Intervention − 
    MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science, vol 10435. Springer, Cham

Contents

  1. Requirements
  2. Training
  3. References
  4. Contact
  5. License

Requirements

To run this code, you will require the following softwares.

  1. Anaconda (for python ver 2.7 ) - A package of useful python libraries
  2. Keras (ver 1.2.1) - A high level neural network library written in python.
  3. Theano (ver 0.9.0) - A python library to efficiently evaluate mathematical expressions on GPU.

Training

  • To train AlexNet or Texture-CNN models [1] prefitted with SD-Layer, follow the code within Code/Train/training.ipynb.
  • The implementation of SD-Layer resides in Code/Train/SDLayer.py.
  • The implementation of SVD based color deconvolution resides in Code/Train/macenko.py. Please refer to [2] for the method details.
  • The implementation of Energy layer resides in Code/Train/energyPool.py. Please refer to [1] for details.

References

[1] Andrearczyk, Vincent, and Paul F. Whelan. "Using filter banks in convolutional neural networks for texture classification." Pattern Recognition Letters 84 (2016): 63-69.

[2] Macenko, Marc, et al. "A method for normalizing histology slides for quantitative analysis." Biomedical Imaging: From Nano to Macro, 2009. ISBI'09. IEEE International Symposium on. IEEE, 2009.

Contact

For any assistance with the code or for reporting errors, please get in touch at rahulduggal2608 [at] gmail [dot] com.

License

This code is released under the MIT License (refer to the LICENSE file for details).

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Code accompanying our MICCAI 2017 paper.

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