Supplementary Online Content. Reducing Confounding Factors’ Impact on Skin Cancer Classification via Image Segmentation.
Published: 17 June 2020| Version 1 | DOI: 10.17632/jdws46cmp3.1
Contributors:
Roman Maron,
Achim Hekler,
Eva Krieghoff-Henning,
Max Schmitt,
Justin Schlager,
Jochen Utikal,
Titus Brinker
Description
Supplementary Table 1. Train and test set class distribution for each dataset source. Supplementary Methods. Segmentation Model and Classifier Development. Supplementary Table 2. Overview of the secondary outcome metrics for each type of ensemble across the holdout, external, and overall test set. Supplementary Table 3. Overview of the secondary outcome metrics for each type of ensemble across the external test set’s three individual components.
Files
Institutions
Deutsches Krebsforschungszentrum
Categories
Diagnosis, Melanoma, Automated Segmentation, Nevus, Convolutional Neural Network, Deep Learning