Repository to reproduce the results on SIM-ISIC Competition 2020 Dataset hosted on kaggle competitions
Steps performed:
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Image Preprocessing:
- Hair Artifact Removal using Bottom Hat Filter and inpainting.
- Color Constancy corrections using Gray world and max RGB algorithms(Originally constructed by LincolnZjx)
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Training CNN: Efficient Nets are trained in both ensemble and stand-alone manner
- Stratified Group K-Fold Cross validation
- Label Smoothing
- Loss: FOCAL LOSS
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Testing CNN:
- Tested using both public and private test data hosted on kaggle competition
Metrics Used:
The same metric AUC which is given in kaggle is used to measure the performance of the model since the dataset is highly skewed.
PROGRESS
- Hair Artifact Removal
- Color Constancy
- Stratified Group K-Fold Split
- Label Smoothing
- Training
- Testing