Skip to content

Research model for classification and feature extraction of dermatoscopic images

License

Notifications You must be signed in to change notification settings

ptschandl/dermatoscopy_resnet34_nmed_2020

Repository files navigation

Dermatoscopic classification model

Image classification model for multi-class prediction of common dermatologic tumors, based on dermatoscopic images. This resnet34 model was trained on the HAM10000 dataset, and used in the publication Tschandl P. et al. Nature Medicine 2020 where we explored human-computer interaction of a classification system.

Entrypoints

  • app.py: Gradio web-app for single-image prediction
  • images_predict_extract.py: Create predictions and feature vectors for images within a folder

For education and research use only. DO NOT use this to obtain medical advice! If you have a skin change in question, seek contact to a health care professional.

Citation

If you use this model, please consider citing the original work for which it was created:

@article{Tschandl2020_NatureMedicine,
  author = {Philipp Tschandl and Christoph Rinner and Zoe Apalla and Giuseppe Argenziano and Noel Codella and Allan Halpern and Monika Janda and Aimilios Lallas and Caterina Longo and Josep Malvehy and John Paoli and Susana Puig and Cliff Rosendahl and H. Peter Soyer and Iris Zalaudek and Harald Kittler},
  title = {Human{\textendash}computer collaboration for skin cancer recognition},
  journal = {Nature Medicine},
  volume = {26},
  number = {8},
  year = {2020},
  pages = {1229--1234},
  doi = {10.1038/s41591-020-0942-0},
  url = {https://doi.org/10.1038/s41591-020-0942-0}
}

About

Research model for classification and feature extraction of dermatoscopic images

Topics

Resources

License

Stars

Watchers

Forks

Languages