3 papers
cs.CV2025
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…
q-bio.QM2025
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…
eess.IV2024
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…