A weakly supervised approach to skin lesion segmentation

Early detection of skin cancers greatly increases patients' chances of recovery. To support dermatologists in this diagnosis, many decision support systems based on Convolutional Neural Networks have recently been proposed to segment the lesion and classify it. The use of the information coming from the segmentation, as an additional input to the classifier, proved to be fundamental to increase its performance and, in fact, the shape of the lesion is of diagnostic importance unanimously recognized by clinicians. However, in the ISIC database, the public reference dataset that collects a huge number of skin lesion images, all samples are labeled for classification but only a very small fraction of them are also labeled for segmentation. To overcome this limitation, the present paper proposes a weakly supervised approach to extract the segmentation label maps of approximately 43,000 ISIC images, used to train a segmentation network, with very promising performance. © 2022 ESANN 2022 Proceedings - 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. All rights reserved.