Leveraging Contextual Data Augmentation for Generalizable Melanoma Detection. (arXiv:2212.05116v3 [eess.IV] UPDATED)

While skin cancer detection has been a valuable deep learning application for
years, its evaluation has often neglected the context in which testing images
are assessed. Traditional melanoma classifiers assume that their testing
environments are comparable to the structured images they are trained on. This
paper challenges this notion and argues that mole size, a critical attribute in
professional dermatology, can be misleading in automated melanoma detection.
While malignant melanomas tend to be larger than benign melanomas, relying
solely on …

application arxiv augmentation cancer cancer detection challenges classifiers context data deep learning detection environments evaluation images notion paper skin cancer testing