Multiple Instance Learning for Histopathological Grading of Penile Cancer - Santos, Rick Eick V.; Martinez, Victor José B. A.; Braz Júnior, Geraldo (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à Saúde, 2026)
Penile cancer exhibits a relatively high incidence in developing countries. In this context, this work proposes a Multiple Instance Learning–based approach for the classification of histopathological images in the PCPAm dataset, addressing both binary cancer detection and histopathological grade classification. The method leverages patch-based decomposition of high-resolution images combined with feature aggregation strategies and class imbalance mitigation techniques, evaluated under stratified five-fold cross-validation. The best configuration achieved over 81.5% accuracy and 80.2% F1-score in histopathological grade classification, establishing a strong baseline for future research in computer-assisted diagnosis of penile cancer.