Yazan, ErsanTalu, Muhammed Fatih2024-08-042024-08-0420231300-70092147-5881https://doi.org/10.5505/pajes.2022.07838https://search.trdizin.gov.tr/yayin/detay/1188624https://hdl.handle.net/11616/92937Innovations such as the widespread use of motorized microscopes, the automatic scanning of the tissue taken from the patient and transferring it to a single large image, and the production of deep/adversarial networks specific to segmentation have increased the hope of automatically producing outputs very close to expert labeling in the segmentation problem. Particularly, it is known that segmentation performances are improved by integrating attention modules into classical 3D-UNet or GAN architectures. In this study, the effects of four different attention modules (DAF, DAF3D, DANet and MSA) were analyzed in solving the histopathological image segmentation problem. While single (SLF) and multiple (MLF) layer features are used together in DAF and DAF3D modules, two different mechanisms, position attention module and channel attention module, are used in DANet and MSA modules. As a result of the experimental studies, it has been seen that the DAF3D attention module maximizes the segmentation accuracy (0.76 mIoU and 0.89 PA). At the same time, the method with the lowest segmentation cost (0.156 seconds for 1 image) among the approaches was again DAF3D.eninfo:eu-repo/semantics/openAccessAttention mechanismHistopathologySegmentationDANetDAFDAF3DArticle29324825510.5505/pajes.2022.078381188624WOS:001018927800004Q4