Data-Centric Evaluation of Image Segmentation Under Dataset Composition Shift - Favoreto, Graciella S.; Koenigkam Santos, Marcel; Traina, Agma J. M. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd.2026.249643, 2026)
Lung segmentation in chest X-ray images is a key step in computer-aided diagnosis, but segmentation quality often degrades under domain shift, particularly in unseen conditions. This work investigates the impact of dataset composition on U-Net-based models trained and evaluated across two public datasets. A basic U-Net and encoder-based variants (ResNet-34, ResNet-50, and VGG-16) are compared under different protocols. Results indicate that segmentation performance is highly sensitive to dataset composition and evaluation protocol, especially in pathological samples. These findings suggest that pathological variability is a major factor in degradation and highlight the importance of data-centric evaluation for reliable benchmarking in medical image analysis.