Publication
Crop It, but Not Too Much: The Effects of Masking on the Classification of Melanoma Images
Fabrizio Nunnari; Abraham Ezema; Daniel Sonntag
In: Stefan Edelkamp; Ralf Möller; Elmar Rueckert (Hrsg.). KI 2021: Advances in Artificial Intelligence. German Conference on Artificial Intelligence (KI-2021), September 27 - October 1, Germany, Pages 179-193, ISBN 978-3-030-87626-5, Springer International Publishing, 2021.
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.
Projekte
pAItient - Protected Artificial Intelligence Innovation Environment for Patient Oriented Digital Health Solutions for developing, testing and evidence based evaluation of clinical value.,
KI-Para-Mi - KI-getriebener Paradigmenwechsel durch Mitarbeiter-zentrische Schicht- und Dienstplanung zur Verringerung des Pflegenotstands
KI-Para-Mi - KI-getriebener Paradigmenwechsel durch Mitarbeiter-zentrische Schicht- und Dienstplanung zur Verringerung des Pflegenotstands