Parallel-Hierarchical Multi-Task Learning for Skin Lesion Classification with Multimodal Data - Dias, Camila Alves; Lermen, Thiago Sotoriva; Stringhini, Rômulo Marconato; Costa, Cristiano Andre da; Jung, Cláudio Rosito; Costa, Manuela M.; Rados, Dimitris V.; Carvalho, Fabiana; Gonçalves, Marcelo R. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
This paper introduces a novel approach for skin cancer detection that leverages both smartphone-captured images and clinical data. The proposed method utilizes a parallel-hierarchical multi-task learning framework to classify skin lesion types and assess malignancy simultaneously. Compared to existing methods reporting false positive rates around 3.6% in the Melanoma Skin Cancer dataset, our approach achieves a false positive rate of 8.2% while maintaining a high true positive rate of 94.03% for malignant lesion detection. These results demonstrate the potential of the approach to enhance diagnostic accuracy and support more effective treatment decisions in teledermatology settings.