Enhancing Anomaly Detection in Melanoma Diagnosis Through Self-Supervised Training and Lesion Comparison - Aix-Marseille Université Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

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

Résumé

Melanoma, 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.
Fichier sous embargo
Fichier sous embargo
0 5 16
Année Mois Jours
Avant la publication
mardi 15 octobre 2024
Fichier sous embargo
mardi 15 octobre 2024
Connectez-vous pour demander l'accès au fichier

Dates et versions

hal-04387404 , version 1 (11-01-2024)

Identifiants

Citer

Jules Collenne, Rabah Iguernaissi, Séverine Dubuisson, Djamal Merad. Enhancing Anomaly Detection in Melanoma Diagnosis Through Self-Supervised Training and Lesion Comparison. Machine Learning in Medical Imaging, Oct 2023, Vancouver, BC, Canada. pp.155-163, ⟨10.1007/978-3-031-45676-3_16⟩. ⟨hal-04387404⟩
25 Consultations
14 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More