Enhancing Anomaly Detection in Melanoma Diagnosis Through Self-Supervised Training and Lesion Comparison

International audienceMelanoma, a highly aggressive form of skin cancer notorious for its rapid metastasis, necessitates early detection to mitigate complex treatment requirements. While considerable research has addressed melanoma diagnosis using convolutional neural networks (CNNs) on individual dermatological images, a deeper exploration of lesion comparison within a patient is warranted for enhanced anomaly detection, which often signifies malignancy. In this study, we present a novel approach founded on an automated, self-supervised framework for comparing skin lesions, working entirely without access to ground truth labels. Our methodology involves encoding lesion images into feature vectors using a state-of-the-art representation learner, and subsequently leveraging an anomaly detection algorithm to identify atypical lesions. Remarkably, our model achieves robust anomaly detection performance on ISIC 2020 without needing annotations, highlighting the efficacy of the representation learner in discerning salient image features. These findings pave the way for future research endeavors aimed at developing better predictive models as well as interpretable tools that enhance dermatologists' efficacy in scrutinizing skin lesions

Computer Aided Diagnosis of Melanoma Using Deep Neural Networks and Game Theory: Application on Dermoscopic Images of Skin Lesions

International audienceEarly detection of melanoma remains a daily challenge due to the increasing number of cases and the lack of dermatologists. Thus, AI-assisted diagnosis is considered as a possible solution for this issue. Despite the great advances brought by deep learning and especially convolutionalneural networks (CNNs), computer-aided diagnosis (CAD) systems are still not used in clinical practice. This may be explained by the dermatologist’s fear of being misled by a false negative and the assimilation of CNNs to a “black box”, making their decision process difficult to understandby a non-expert. Decision theory, especially game theory, is a potential solution as it focuses on identifying the best decision option that maximizes the decision-maker’s expected utility. This study presents a new framework for automated melanoma diagnosis. Pursuing the goal of improvingthe performance of existing systems, our approach also attempts to bring more transparency in the decision process. The proposed framework includes a multi-class CNN and six binary CNNs assimilated to players. The players’ strategies is to first cluster the pigmented lesions (melanoma,nevus, and benign keratosis), using the introduced method of evaluating the confidence of the predictions, into confidence level (confident, medium, uncertain). Then, a subset of players has the strategy to refine the diagnosis for difficult lesions with medium and uncertain prediction. We usedEfficientNetB5 as the backbone of our networks and evaluated our approach on the public ISIC dataset consisting of 8917 lesions: melanoma (1113), nevi (6705) and benign keratosis (1099). The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.93 for melanoma, 0.96 for nevus and 0.97 for benign keratosis. Furthermore, our approach outperformed existing methods in this task, improving the balanced accuracy (BACC) of the best compared method from 77% to 86%. These results suggest that our framework provides an effective and explainable decision-making strategy. This approach could help dermatologists in their clinical practice for patients with atypical and difficult-to-diagnose pigmented lesions. We also believe that our system could serve as a didactic tool for less experienced dermatologists