Comparative Analysis of Deep Learning Architectures and Morphological Pre-processing for Prostate Cancer Histopathology - Silva, Maxwell Gomes da; Travençolo, Bruno Augusto Nassif; Backes, André R. (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à Saúde, 2026)
Prostate cancer remains one of the main causes of male mortality worldwide. The histopathological assessment based on hematoxylin-eosin (H&E) staining continues to be the diagnostic gold standard. However, the process is subjective and computationally demanding because of the size and complexity of Whole Slide Images (WSIs). This paper presents a comparative study of deep learning architectures and morphological pre-processing strategies for prostate cancer histopathology. The proposed approach combines a morphological pre-processing stage with a Mask R-CNN model implemented in Detectron2 and compares its performance with other benchmark architectures, including GAN-based segmentation, hierarchical transformers (HIPT), Multiple Instance Learning (MIL), and the CrowdGleason framework. Performance was evaluated using accuracy, F1-score, Intersection over Union (IoU), and Cohen’s Kappa. The integration of morphological operations with Mask R-CNN achieved the best results, reaching 97.87% accuracy and a 0.96 F1-score. These findings reinforce how domain-guided enhancement can significantly improve segmentation quality and generalization in digital histopathology