Dermoscopic dark corner artifacts removal: Friend or foe?

Pewton, SW, Cassidy, B ORCID: https://orcid.org/0000-0003-3741-8120, Kendrick, C ORCID: https://orcid.org/0000-0002-3623-6598 and Yap, MH ORCID: https://orcid.org/0000-0001-7681-4287 (2024) Dermoscopic dark corner artifacts removal: Friend or foe? Computer Methods and Programs in Biomedicine, 244. 107986. ISSN 0169-2607

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Abstract

Background and Objectives: 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 a lack of available datasets with corresponding labels for dark corner artifact cases. Methods: 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. Results: 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. Conclusions: The proposed artifact methods can be used in future experiments to help alleviate possible impacts on model performance. Additionally, the newly proposed heatmap quantification analysis will help to better understand the relationships between heatmap results and other model performance metrics.