Automated mitosis detection in stained histopathological images using Faster R-CNN and stain techniques

García-Salmerón, Jesús; García, José Manuel; Bernabé, Gregorio; González Férez, Pilar

Publication:
Automated mitosis detection in stained histopathological images using Faster R-CNN and stain techniques

dc.contributor.author	García-Salmerón, Jesús
dc.contributor.author	García, José Manuel
dc.contributor.author	Bernabé, Gregorio
dc.contributor.author	González Férez, Pilar
dc.contributor.department	Ingeniería y Tecnología de Computadores	es
dc.date.accessioned	2025-06-11T12:03:31Z
dc.date.available	2025-06-11T12:03:31Z
dc.date.issued	2025-06-11
dc.description	© 2025 the author(s). This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/. This document is the Published version of a Published Work that appeared in final form in Journal of Integrative Bioinformatics. To access the final edited and published work see https://doi.org/10.1515/jib-2024-0049
dc.description.abstract	Accurate mitosis detection is essential for cancer diagnosis and treatment. Traditional manual counting by pathologists is time-consuming and may cause errors. This research investigates automated mitosis detection in stained histopathological images using Deep Learning (DL) techniques, particularly object detection models. We propose a two-stage object detection model based on Faster R-CNN to effectively detect mitosis within histopathological images. The stain augmentation and normalization techniques are also applied to address the significant challenge of domain shift in histopathological image analysis. The experiments are conducted using the MIDOG++ dataset, the most recent dataset from the MIDOG challenge. This research builds on our previous work, in which two one-stage frameworks, in particular on RetinaNet using fastai and PyTorch, are proposed. Our results indicate favorable F1-scores across various scenarios and tumor types, demonstrating the effectiveness of the object detection models. In addition, Faster R-CNN with stain techniques provides the most accurate and reliable mitosis detection, while RetinaNet models exhibit faster performance. Our results highlight the importance of handling domain shifts and the number of mitotic figures for robust diagnostic tools.	es
dc.format	application/pdf	es
dc.format.extent	15	es
dc.identifier.citation	Journal of Integrative Bioinformatics 2025; 20240049
dc.identifier.doi	https://doi.org/10.1515/jib-2024-0049
dc.identifier.issn	Electronic: 1613-4516
dc.identifier.uri	http://hdl.handle.net/10201/155804
dc.language	eng	es
dc.publisher	De Gruyter
dc.relation	Ámbito: Proyecto nacional. Financiador: Agencia Estatal de Investigación. Convocatoria: Proyectos Estratégicos Orientados a la Transición Ecológica y a la Transición Digital. Nombre: Aplicación de la Computación Eficiente de Alto Rendimiento con Técnicas Avanzadas de Inteligencia Artificial para el Diagnóstico de Enfermedades en Sistemas Heterogéneos. This work has been partially funded by Grant TED2021-129221B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”.	es
dc.relation.publisherversion	https://www.degruyterbrill.com/document/doi/10.1515/jib-2024-0049/html	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Atribución 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.subject	Artificial Intelligence	es
dc.subject	Cancer	es
dc.subject	MIDOG Challenge	es
dc.subject	Object detection	es
dc.subject	Tumor prognosis	es
dc.title	Automated mitosis detection in stained histopathological images using Faster R-CNN and stain techniques	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es