Deep Convolutional Neural Network for Accurate Classification of Myofibroblastic Lesions on Patch-Based Images

Autor(es)

Giraldo-Roldan, Daniela ; dos Santos, Giovanna Calabrese ; Araujo, Anna Luiza Damaceno ; Nakamura, Thais Cerqueira Reis ; Pulido-Diaz, Katya ; Lopes, Marcio Ajudarte ; Santos-Silva, Alan Roger ; Kowalski, Luiz Paulo ; Moraes, Matheus Cardoso ; Vargas, Pablo Agustin

• Tipo de documento: Artigo Científico
• Fonte: Head and Neck Pathology; v. 18, n. 1, p. 9-pg., 2024-10-28.

Resumo

ObjectiveThis study aimed to implement and evaluate a Deep Convolutional Neural Network for classifying myofibroblastic lesions into benign and malignant categories based on patch-based images.MethodsA Residual Neural Network (ResNet50) model, pre-trained with weights from ImageNet, was fine-tuned to classify a cohort of 20 patients (11 benign and 9 malignant cases). Following annotation of tumor regions, the whole-slide images (WSIs) were fragmented into smaller patches (224 x 224 pixels). These patches were non-randomly divided into training (308,843 patches), validation (43,268 patches), and test (42,061 patches) subsets, maintaining a 78:11:11 ratio. The CNN training was caried out for 75 epochs utilizing a batch size of 4, the Adam optimizer, and a learning rate of 0.00001.ResultsResNet50 achieved an accuracy of 98.97%, precision of 99.91%, sensitivity of 97.98%, specificity of 99.91%, F1 score of 98.94%, and AUC of 0.99.ConclusionsThe ResNet50 model developed exhibited high accuracy during training and robust generalization capabilities in unseen data, indicating nearly flawless performance in distinguishing between benign and malignant myofibroblastic tumors, despite the small sample size. The excellent performance of the AI model in separating such histologically similar classes could be attributed to its ability to identify hidden discriminative features, as well as to use a wide range of features and benefit from proper data preprocessing. (AU)

• Processo FAPESP: 09/53839-2 - Criação do Laboratório de Patologia Digital através do uso do escaneador de lâminas histológicas (Aperio® Scanscope CS)
• Beneficiário: Oslei Paes de Almeida
• Modalidade de apoio: Auxílio à Pesquisa - Programa Equipamentos Multiusuários
• Processo FAPESP: 21/14585-7 - Inteligência artificial aplicada ao diagnóstico clínico e histopatológico do Câncer de Cabeça e Pescoço
• Beneficiário: Anna Luiza Damaceno Araujo
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado