A Transformer-Based Method for Semantic Segmentation of Mitosis in Breast Histopathology Images

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dc.contributor.authorKara Ardaç, Fatma Betül
dc.contributor.authorErdoǧmuş, Pakize
dc.date.accessioned2026-07-01T11:36:27Z
dc.date.available2026-07-01T11:36:27Z
dc.date.issued2025
dc.departmentDüzce Üniversitesi
dc.description9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025 -- 6 September 2025 through 7 September 2025 -- Malatya -- 215321
dc.description.abstractAccording to the World Health Organization's 2022 reports, approximately 2.3 million women globally have received a diagnosis of breast cancer. The detection of mitosis is critical for assessing tumor proliferation rates, which serve as vital biomarkers in breast cancer diagnosis. Consequently, numerous studies have concentrated on the development of deep learning models to automate the identification of mitotic cells in breast cancer tissues. Deep learning methodologies such as U-Net and Mask R-CNN have demonstrated effectiveness in enhancing mitosis detection within breast cancer histopathology images. In this study, SAM and SegFormer, which are based on transformer architecture and have recently shown superior performance, were used. The ICPR14 dataset, an open-access breast histopathology dataset, was employed for the training of SegFormer. Given that the ICPR14 dataset is originally an object detection dataset, it was converted into a segmentation dataset utilizing SAM. Six distinct experiments were conducted to ascertain the optimal performance for mitosis detection. The experiments analyzed the influence of varying encoder sizes on model performance. Evaluation metrics including precision, recall, F1 score, and Intersection over Union (IoU) were employed for the comparative analysis of experimental results. The findings revealed that the Segformer-B4 model, which utilized a channel size of 512 and the MiT-B4 encoder, attained the highest performance metrics. Specifically, the Segformer-B4 model achieved precision of 0.8994, recall of 0.8931, F1 score of 0.8962, and IoU of 0.9063. These results indicate that the SegFormer approach can effectively facilitate mitosis detection in breast histopathology images. © 2025 IEEE.
dc.identifier.doi10.1109/IDAP68205.2025.11222172
dc.identifier.isbn979-833158990-5
dc.identifier.scopus2-s2.0-105025043017
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP68205.2025.11222172
dc.identifier.urihttps://hdl.handle.net/20.500.12684/23040
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260623
dc.subjectbreast histopathology
dc.subjectmitosis detection
dc.subjectsegFormer
dc.subjectsemantic segmentation
dc.subjecttransformer
dc.titleA Transformer-Based Method for Semantic Segmentation of Mitosis in Breast Histopathology Images
dc.typeConference Object

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