Skin lesion analysis on the use of contextual information for melanoma identi cation in dermoscopic images

Skin lesions are a severe disease globally. Early detection of melanoma in dermatoscopy im-ages significantly increases the survival rate. However, the accurate recognition of melanomais extremely challenging due to the following reasons: low contrast between lesions and skin,appearances of artifacts, etc. Hence, reliable automatic detection of skin tumors is very usefulto increase the accuracy and efficiency of dermatologists. In this MSc thesis we explore the useof contextual information to improve the performance of Deep Learning classifiers in the areaof skin lesion image classification. This information was obtained by means of patient embed-dings, attention, and a hand-crafted contextual sampler. The proposed methods were evaluatedon the ISIC 2020 dataset. Experimental results show that this techniques yield better results butthey are still not significant enough. It is necessary to make future research on other ways toaggregate this information.