Publication: Automated mitosis detection in stained histopathological images using Faster R-CNN and stain techniques
Authors
García-Salmerón, Jesús ; García, José Manuel ; Bernabé, Gregorio ; González Férez, Pilar
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Publisher
De Gruyter
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DOI
https://doi.org/10.1515/jib-2024-0049
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info:eu-repo/semantics/article
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
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.
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Citation
Journal of Integrative Bioinformatics 2025; 20240049
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Este ítem está sujeto a una licencia Creative Commons. http://creativecommons.org/licenses/by/4.0/