29 citations · 54 across the 5 of their papers we have counts for
5 papers
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…
Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples
David Macêdo, Tsang Ing Ren, Cleber Zanchettin +2
In this paper, we argue that the unsatisfactory out-of-distribution (OOD) detection performance of neural networks is mainly due to the SoftMax loss anisotropy and propensity to pr…
Entropic Out-of-Distribution Detection
David Macêdo, Tsang Ing Ren, Cleber Zanchettin +2
Out-of-distribution (OOD) detection approaches usually present special requirements (e.g., hyperparameter validation, collection of outlier data) and produce side effects (e.g., cl…