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Melanoma Classification Web App

This repository houses the code for a streamlit powered web app (capable of running on an AWS t2.micro EC2 instance) backed with a CNN fine-tuned on the SIIM ISIC Melanoma Classification Competition data.

demo ss

Model Training:

  • Note: Training code is NOT available in this repository.
  • The CNN used is a resnest50_fast_4s1x64d variant of ResNeSt family of CNNs published in ResNeSt: Split-Attention Networks by Hang Zhang et al.
  • The network is trained on an NVIDIA P100 TENSOR CORE GPU provided by Kaggle in the GPU accelerator version of Kaggle Kernels, however the weight tensors are converted from cuda tensors to CPU tenesors to allow for inferring on machines without a GPU.
  • The network is trained on two thirds of the ISIC 2020 JPEG images and all the JPEG images of ISIC 2019 (and 2018) resized to 128*128 sq. pixels for 15 epochs with a batch size of 256.
  • The model reaches a validation AUC (calculated on the third part of ISIC 2020 data dropped from train set) of 0.8892 with single inference and 0.9010 with Test Time Augmentations.
  • Providing better model weights is WIP.

Getting Started:

Follow same steps to run the web app on a cloud vm.

  • git clone this repository.
  • [optional but recommended] Set up a virtual environement.
  • Run pip install -r requiments.txt to install all* the python libraries.
    • *opencv-python needs to be installed using these steps.
  • Download the weights of the Neural Network from here.
  • Run streamlit run app.py <path/of/the/weights_file(.ckpt)> in the system CLI.
  • In a web browser of choice, open localhost:8501.

Built with:

  • Python
  • PyTorch: torch + torchvision
  • Albumentations
  • Numpy
  • Streamlit

Author(s):

Puneet Singh

Acknowledgements:

  • Zhang, H., Wu, C., Zhang, Z., Zhu, Y., Zhang, Z., Lin, H., Sun, Y., He, T., Muller, J., Manmatha, R., Li, M., & Smola, A. (2020). ResNeSt: Split-Attention NetworksarXiv preprint arXiv:2004.08955.
  • Buslaev, A., Iglovikov, V., Khvedchenya, E., Parinov, A., Druzhinin, M., & Kalinin, A. (2020). Albumentations: Fast and Flexible Image AugmentationsInformation, 11(2).
  • Falcon, W. (2019). PyTorch LightningGitHub. Note: https://github.com/PyTorchLightning/pytorch-lightning Cited by, 3.