Computer Aided Diagnosis of Skin Lesions

PyTorch Lightning • PyTorch • Hydra •

The repository contains results and code for the project skin lesion classification, which includes 2 challenges:

• Challenge 1: Binary classification: Nevus vs. Others
• Challenge 2: Three class problem: Basal Cell vs. Squamous Cell vs. Melanoma

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Our solution ranked top-1 on a private leaderboard for the binary classification challenge for both ML and DL implementations while our DL also solution ranked top 3 for the 3-class imbalanced problem

The presentation of results and approaches can be seen in following pdfs:

• Part 1: ML and Bag of Visual Words

  • Manual feature engineering, extraction and analysis of ABCD rule features and their importance, lesion segmentation and hair removal pipelines (with grayscale morphology), experiments with the custom Bag of Visual Words implementation on subsets of features and final model ensembling and tackling the imbalances

• Part 2: Deep Learning and pretext training

  • Comparison between the best CNN (RegNetY) and transformer (Swin) model's performances; models and loss tuning; bottleneck embedding fusion experiments and pretext training on a subset of classes

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The data for the challenge was provided by the course organizers. It consisted of images from HAM10000 Dataset (ViDIR Group, Medical University of Vienna), the BCN_20000 Dataset (Hospital Clínic de Barcelona) and the MSK Dataset (ISBI 2017).

For the binary classification problem we had ~15,000 images while for the highly imbalanced 3-class only ~5,000 images from following categories.

Contributors

• Vladyslav Zalevskyi GitHub | LinkedIn
• Manasi Kattel GitHub | LinkedIn

Reproduction details

Set up the environment

Rung following code to set up a conda environment with all the packages needed to run the project.

conda update -n base -c defaults conda &&
conda create -n cad_skin anaconda &&
conda activate cad_skin && 
pip install -r requirements.txt

Download the data

Run skin-lesion-cad/data/make_dataset.py to download data sets and extract them into corresponding folders

python skin-lesion-cad/data/make_dataset.py

Or alternatively, download the data sets manually and extract them into corresponding folders.

At the end the following structure of the /data folder should be created:

data/
├── processed
└── raw
    ├── chall1
    │   ├── train
    │   │   ├── nevus
    │   │   └── others
    │   └── val
    │       ├── nevus
    │       └── others
    └── chall2
        ├── train
        │   ├── bcc
        │   ├── mel
        │   └── scc
        └── val
            ├── bcc
            ├── mel
            └── scc

18 directories

Segment the images

Performs basic preprocessing in images:

• hair removal
• inpainting
• segmentation

Saves inpainted image and mask in corresponding folders in the /data/processed.

Run script

$ python -m skin_lesion_cad.utils.segm_script chall1/train/nevus

from repo root ../skin-lesion-cad$. Parameter passed (chall1/train/nevus) defines which images from which folder to process.

Could also pass --resize option to resize images by a factor. For example to downscale image by 2 run

python -m skin_lesion_cad.utils.segm_script chall1/train/nevus --resize 0.5

Bag of Visual Words

DenseDescriptor and BagofWords class can be found in skin_lesion_cad/data/BOVW.py. Example and experimental notebooks for BoVW: 0.1_mk_BOVW.ipynb, 0.3_mk_lbp_bow.ipynb, BoW_Fusions.ipynb, ColorBoW.ipynb, ColorBoW_3cls.ipynb.

Features

The code for feature classes can be found in skin_lesion_cad/colour.py, skin_lesion_cad/texture.py and skin_lesion_cad/shape.py. To extract and save all the features run python -m skin_lesion_cad.features.extract_features. Experimental notebooks for feature selection: 0.5_mk_feature_selection.ipynb and FeatureFusionExperiments.ipynb.

Training

Ensemble class for training the models can be found in skin-lesion-cad/data/ensemble.py. The notebooks for tuning the models and final prediction generation:

• Challenge 1: 1.3_mk_chall1_finalize.ipynb, 1.4_mk_finalize_only_pred_noexp.ipynb
• Challenge 2: Challenge2_FinalPipeLine.ipynb, FinalTestResults.ipynb, 1.2_mk_chall2_pred_test.ipynb

Deep Learning

Take a look at ./skin_lesion_cad/training/models to see the DL models implementations.

Running Experiments

1. Update corresponding config files train_config.yaml, regNet.yaml and skin.yaml in skin_lesion_cad/configs/ folder. Don't forget to change the hydra.job.name to a unique name for each experiment.
2. Run the following command from the root of the repo ../skin-lesion-cad$ to run the experiment:

python3 -m skin_lesion_cad.training.train_ch1

Visualizing data

1. Update the configstrain_config.yaml, regNet.yaml and skin.yaml in skin_lesion_cad/configs/ to display required images.
2. Run the following command from the root of the repo ../skin-lesion-cad$

python3 -m skin_lesion_cad.visualization.vis_dat_simple