Abstract
To improve the accuracy of convolutional neural networks in discriminating between nevi and melanomas, we test nine different combinations of masking and cropping on three datasets of skin lesion images (ISIC2016, ISIC2018, and MedNode). Our experiments, confirmed by 10-fold cross-validation, show that cropping increases classification performances, but specificity decreases when cropping is applied together with masking out healthy skin regions. An analysis of Grad-CAM saliency maps shows that in fact our CNN models have the tendency to focus on healthy skin at the border when a nevus is classified.
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Acknowledgments
The research has been supported by the Ki-Para-Mi project (BMBF, 01IS19038B), the pAItient project (BMG, 2520DAT0P2), and the Endowed Chair of Applied Artificial Intelligence, Oldenburg University (see https://uol.de/aai/. We would like to thank all student assistants that contributed to the development of the platform (see https://iml.dfki.de/).
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Appendices
A MedNode Results
Table 3 and Table 4 show the results of the experiments on the MedNode dataset.
B ISIC2016 Results
Table 5 and Table 6 show the results of the experiments on the ISIC2016 dataset.
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Nunnari, F., Ezema, A., Sonntag, D. (2021). Crop It, but Not Too Much: The Effects of Masking on the Classification of Melanoma Images. In: Edelkamp, S., Möller, R., Rueckert, E. (eds) KI 2021: Advances in Artificial Intelligence. KI 2021. Lecture Notes in Computer Science(), vol 12873. Springer, Cham. https://doi.org/10.1007/978-3-030-87626-5_13
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DOI: https://doi.org/10.1007/978-3-030-87626-5_13
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