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Importance of complementary data to histopathological image analysis of oral leukoplakia and carcinoma using deep neural networks

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Intelligent Medicine 2023年第4期3卷 258-266页

作者： Leandro Muniz de Lima Maria Clara Falcão Ribeiro de Assis júlia pessini soares Tânia Regina Grão-Velloso Liliana Aparecida Pimenta de Barros Danielle Resende Camisasca Renato Antonio Krohling Nature-inspired Computing Lab Federal University of Espirito SantoVitoriaBrazil Graduate Program in Computer Science Federal University of Espirito SantoVitoriaBrazil School of Dentistry Departamento de Clinica OdontologicaFederal University of Espirito SantoVitoriaBrazil Graduate Program in Clinical Dentistry Federal University of Espirito SantoVitoriaBrazil

Background Oral cancer is one of the most common types of cancer in men causing mortality if not *** recent years,computer-aided diagnosis(CAD)using artificial intelligence techniques,in particular,deepneural networks have been investigated and several approaches have been proposed to deal with the automateddetection of various pathologies using digital *** studies indicate that the fusion of images with thepatient’s clinical information is important for the final clinical *** such dataset does not yet exist fororal cancer,as far as the authors are aware,a new dataset was collected consisting of histopathological images,demographic and clinical *** study evaluated the importance of complementary data to histopathologicalimage analysis of oral leukoplakia and carcinoma for *** A new dataset(NDB-UFES)was collected from 2011 to 2021 consisting of histopathological imagesand *** 237 samples were curated and analyzed by oral pathologists generating the gold standardfor ***-of-the-art image fusion architectures and complementary data(Concatenation,MutualAttention,MetaBlock and MetaNet)using the latest deep learning backbones were investigated for 4 distincttasks to identify oral squamous cell carcinoma,leukoplakia with dysplasia and leukoplakia without *** evaluate them using balanced accuracy,precision,recall and area under the ROC curve *** Experimental results indicate that the best models present balanced accuracy of 83.24%using images,demographic and clinical information with MetaBlock fusion and ResNetV2 *** represents an improvement in performance of 30.68%(19.54 pp)in the task to differentiate samples diagnosed with oral squamous cellcarcinoma and leukoplakia with or without *** This study indicates that cured demographic and clinical data may positively influence the performance of artificial intelligence models in automated classification of

关键词： Leukoplakia Squamous carcinoma Pathology Deep learning Transfer learning

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