Hard Sample Mining to Identify Challenging Samples for Breast Cancer Tumor Classification - S. Uchida, Mariana Aya; S. Lima, Afonso Matheus; Sousa, Elaine P. M. de; Traina, Agma J. M. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
Deep learning has greatly advanced image classification in medical imaging, aiding the early detection of breast tumors. However, training deep learning models still presents challenges related to model efficiency and biases in data distribution. Breast tumor images highlight these challenges due to their complex tissue structures and overlapping cells. Hard Sample Mining (HSM) has emerged as a promising approach, focusing on selecting representative samples by ranking them based on loss or similarity. This work presents an experimental analysis of the effects of HSM on a binary breast tumor classification task using the BreakHis dataset. The results shows the method’s potential for sample ranking in training set pruning.