Dermoscopic Dark Corner Artifacts Removal: Friend or Foe?
Samuel William Pewton Bill Cassidy Connah Kendrick Moi Hoon Yap
One of the more significant obstacles in classification of skin cancer is the
presence of artifacts. This paper investigates the effect of dark corner
artifacts, which result from the use of dermoscopes, on the performance of a
deep learning binary classification task. Previous research attempted to remove
and inpaint dark corner artifacts, with the intention of creating an ideal
condition for models. However, such research has been shown to be inconclusive
due to lack of available datasets labelled with dark corner artifacts and
detailed analysis and discussion. To address these issues, we label 10,250 skin
lesion images from publicly available datasets and introduce a balanced dataset
with an equal number of melanoma and non-melanoma cases. The training set
comprises 6126 images without artifacts, and the testing set comprises 4124
images with dark corner artifacts. We conduct three experiments to provide new
understanding on the effects of dark corner artifacts, including inpainted and
synthetically generated examples, on a deep learning method. Our results
suggest that introducing synthetic dark corner artifacts which have been
superimposed onto the training set improved model performance, particularly in
terms of the true negative rate. This indicates that deep learning learnt to
ignore dark corner artifacts, rather than treating it as melanoma, when dark
corner artifacts were introduced into the training set. Further, we propose a
new approach to quantifying heatmaps indicating network focus using a root mean
square measure of the brightness intensity in the different regions of the
heatmaps. This paper provides a new guideline for skin lesions analysis with an
emphasis on reproducibility.