5 papers
Bridging Research and Practice: A Systematic Evaluation of Generalist and Dermatology-Specific Models in Clinical Skin Lesion Classification
Emanoel dos Santos, Kelvin Cunha, Rodrigo Mota +8
The application of machine learning to dermatology has grown substantially in recent years, moving beyond proof-of-concept studies toward potential applications. However, clinical…
Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions
Rodrigo Mota, Kelvin Cunha, Emanoel dos Santos +6
Deep learning models for dermatological image analysis remain sensitive to acquisition variability and domain-specific visual characteristics, leading to performance degradation wh…
DerMAE: Improving skin lesion classification through conditioned latent diffusion and MAE distillation
Francisco Filho, Kelvin Cunha, Fábio Papais +6
Skin lesion classification datasets often suffer from severe class imbalance, with malignant cases significantly underrepresented, leading to biased decision boundaries during deep…
DermAI: Clinical dermatology acquisition through quality-driven image collection for AI classification in mobile
Thales Bezerra, Emanoel Thyago, Kelvin Cunha +9
AI-based dermatology adoption remains limited by biased datasets, variable image quality, and limited validation. We introduce DermAI, a lightweight, smartphone-based application t…
An analysis of data variation and bias in image-based dermatological datasets for machine learning classification
Francisco Filho, Emanoel Santos, Rodrigo Mota +15
AI algorithms have become valuable in aiding professionals in healthcare. The increasing confidence obtained by these models is helpful in critical decision demands. In clinical de…