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…
cs.CL2025
DYNAMAX: Dynamic computing for Transformers and Mamba based architectures
Miguel Nogales, Matteo Gambella, Manuel Roveri
Early exits (EEs) offer a promising approach to reducing computational costs and latency by dynamically terminating inference once a satisfactory prediction confidence on a data sa…
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…