Dark Corner on Skin Lesion Image Dataset: Does It Matter?
Skin lesion image datasets gained popularity in recent years with the successes of ISIC datasets and challenges. While the users of these datasets are growing, the Dark Corner Artifact (DCA) phenomenon is under explored. This paper provides a better understanding of how and why DCA occurs, the types of DCAs and investigates the DCA within a curated ISIC image dataset. We introduce new labels of image artifacts on a curated balanced dataset of 9,810 images and identified 2,631 images with different intensities of DCA. Then, we improve the quality of this dataset by introducing automated DCA detection and removal methods. We evaluate the performance of our methods with image quality metrics on an unseen dataset (Dermofit), and achieved better SSIM score in every DCA intensity level. Further, we study the effects of DCA removal on a binary classification task (melanoma vs non-melanoma). Although deep learning performances in this task show marginal differences, we demonstrate that with DCA removal, it can help to shift the network activations to the skin lesions. All the artifact labels and codes are available at: https://github.com/Sam-Pewton/Dark_Corner_Artifact_Removal.