5 papers
Mitigating Overfitting in Medical Imaging: Self-Supervised Pretraining vs. ImageNet Transfer Learning for Dermatological Diagnosis
Iván Matas, Carmen Serrano, Miguel Nogales +4
Deep learning has transformed computer vision but relies heavily on large labeled datasets and computational resources. Transfer learning, particularly fine-tuning pretrained model…
Discriminating BCC Subtypes Using Entropy and Mutual Information from Dermoscopic Features
Iván Matas, Begoña Acha, Francisca Silva-Clavería +3
Objective: To analyze the frequency and co-occurrence of dermoscopic patterns in BCC lesions and their relationship with histopathologic subtypes, using statistical analysis and In…
MultiTask Learning AI system to assist BCC diagnosis with dual explanation
Iván Matas, Carmen Serrano, Francisca Silva +3
Basal cell carcinoma (BCC) accounts for about 75% of skin cancers. The adoption of teledermatology protocols in Spanish public hospitals has increased dermatologists' workload, mot…
Concordance in basal cell carcinoma diagnosis. Building a proper ground truth to train Artificial Intelligence tools
Francisca Silva-Clavería, Carmen Serrano, Iván Matas +3
Background: The existence of different basal cell carcinoma (BCC) clinical criteria cannot be objectively validated. An adequate ground-truth is needed to train an artificial intel…
Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques
Miguel Nogales, Begoña Acha, Fernando Alarcón +2
This study focuses on analyzing dermoscopy images to determine the depth of melanomas, which is a critical factor in diagnosing and treating skin cancer. The Breslow depth, measure…