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Source code for the paper: "Dermoscopic Dark Corner Artifacts Removal: Friend or Foe?"

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Dermoscopic Dark Corner Artifacts Removal: Friend or Foe?

Citation

If you use any methods, data, or code from this repository please consider citing our paper:

@article{pewton2023dca,
  title = {Dermoscopic dark corner artifacts removal: Friend or foe?},
  journal = {Computer Methods and Programs in Biomedicine},
  volume = {244},
  pages = {107986},
  year = {2024},
  issn = {0169-2607},
  doi = {https://doi.org/10.1016/j.cmpb.2023.107986},
  author = {Samuel William Pewton and Bill Cassidy and Connah Kendrick and Moi Hoon Yap}
}

Masks

If you only require the dark corner artifact masks from these experiments to use in your own dataset, they can be downloaded from the following Kaggle Database repository:

https://www.kaggle.com/datasets/mmucomputervision/dark-corner-artifact-masks-for-isic-images

Requirements

  1. Datasets: - ISIC unbalanced dataset (Duplicates removed).. follow guide at https://github.com/mmu-dermatology-research/isic_duplicate_removal_strategy - save this dataset within the Data directory. - Fitzpatrick 17k.. follow guide at https://github.com/mattgroh/fitzpatrick17k - save this dataset within the Data directory. - DCA Masks.. use "Generate all DCA masks" method at https://github.com/mmu-dermatology-research/dark_corner_artifact_removal and save results within Data directory. ./Data/DCA_Masks/
  2. Models:
  3. Installations:
    • Python 3.9.7
      • Anaconda 4.11.0
      • pandas 1.3.5
      • numpy 1.21.5
      • scikit-learn 1.0.2
      • scikit-image 0.16.2
      • Jupyter Notebook
      • matplotlib 3.5.0
      • OpenCV 4.5.5
      • Pillow 8.4.0
      • Tensorflow 2.9.0-dev20220203
      • Tensorflow-GPU 2.9.0-dev20220203
      • CUDA 11.2.1
      • CuDNN 8.1
      • Keras

Generating the dca split dataset

  1. Open "./Modules/create_balanced_dca_dataset.py" module
  2. Read through docstring for module carefully - changing filepaths as necessary
  3. Execute the module

Project Steps

  1. Train the models: train three InceptionResNetV2 networks on each of the training/validation sets to form a model on the clean set, a model on the binary dca set, and a model on the realistic dca set. Refer to the paper for more information on the network hyper-parameters.
  2. Score the models: score the each of the models on each of the individual test sets, this can be done with the model_performance.py module.
  3. Extract the gradcam heatmaps from all images: run the extract_gradcam.ipynb notebook. (ensure that all of the required filepaths are uncommented)
  4. Calculate the brightness intensities for each of the test set images: modify the base image filepath in the split_intensity.py module to reflect the root folder of the extracted heatmaps. Run the script to generate a .csv file for the internal and external brightness measures for each image. Once this is complete, run the calculate_intensity_averages.py module to calculate the averages across all of the images.

Full Model Performances on all individual testing sets:

Model Used Test Set Metrics Micro-Average
AccTPRTNRF1AUCPrecision
Cleanbase-small0.590.860.320.680.630.56
ns-small0.590.860.310.680.620.56
telea-small0.590.860.310.680.620.56
base-medium0.570.910.240.680.640.54
ns-medium0.620.880.360.700.680.58
telea-medium0.620.870.360.690.680.58
base-large0.510.990.010.670.580.50
ns-large0.640.850.440.700.710.60
telea-large0.650.850.450.710.710.61
base-oth0.580.900.260.670.650.55
ns-oth0.580.870.290.670.660.55
telea-oth0.580.870.290.670.660.55
Binary DCAbase-small0.610.900.330.700.670.57
ns-small0.610.890.330.700.670.57
telea-small0.610.890.330.700.670.57
base-medium0.630.940.310.720.680.58
ns-medium0.650.850.440.710.730.60
telea-medium0.650.850.450.700.730.61
base-large0.550.960.130.680.620.53
ns-large0.700.790.610.730.750.67
telea-large0.700.780.610.720.750.67
base-oth0.600.830.360.670.670.57
ns-oth0.600.820.390.670.680.57
telea-oth0.600.820.390.670.680.57
Realistic DCAbase-small0.600.850.350.680.650.57
ns-small0.600.850.350.680.660.57
telea-small0.600.840.360.680.660.57
base-medium0.640.750.530.680.700.62
ns-medium0.660.840.480.710.720.62
telea-medium0.660.820.490.710.730.62
base-large0.600.390.800.490.630.66
ns-large0.660.700.630.680.740.65
telea-large0.670.690.650.670.740.66
base-oth0.580.810.350.660.650.55
ns-oth0.580.790.370.650.650.56
telea-oth0.580.790.370.650.650.56

References

@article{groh2021evaluating,
  title   = {Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset},
  author  = {Groh, Matthew and Harris, Caleb and Soenksen, Luis and Lau, Felix and Han, Rachel and Kim, Aerin and Koochek, Arash and Badri, Omar},
  journal = {arXiv preprint arXiv:2104.09957},
  year    = {2021}
}

@article{cassidy2021isic,
 title   = {Analysis of the ISIC Image Datasets: Usage, Benchmarks and Recommendations},
 author  = {Bill Cassidy and Connah Kendrick and Andrzej Brodzicki and Joanna Jaworek-Korjakowska and Moi Hoon Yap},
 journal = {Medical Image Analysis},
 year    = {2021},
 issn    = {1361-8415},
 doi     = {https://doi.org/10.1016/j.media.2021.102305},
 url     = {https://www.sciencedirect.com/science/article/pii/S1361841521003509}
} 

@misc{rosebrock_2020, 
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 journal = {PyImageSearch}, 
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@inproceedings{lim2017enhanced,
  title     = {Enhanced deep residual networks for single image super-resolution},
  author    = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Mu Lee, Kyoung},
  booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
  pages     = {136--144},
  year      = {2017}
}

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