Pyramidal position attention model for histopathological image segmentation
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The level of performance achieved in the classification of histopathological images has not yet been reached in the segmentation area. This is because the global context information sufficient for classification is not sufficient for segmentation. Especially, high tissue diversity in histopathological images and the fact that tissues in the same class have quite different colors, patterns and geometries make the segmentation problem difficult. In this study, a novel hybrid architecture (PAMSegNet) is presented that provides high segmentation accuracy in histopathological images. This architecture, which has a pyramid data processing strategy, has been provided with the Position Attention Module (PAM) and Boundary aware Module (BM) to extract global and local attri-butes more accurately. In addition, with the deep supervised technique used, both contents (global and local) were evaluated together in the segmentation decision. Segmentation architectures (Deeplabv3 +, SegNet, U-Net) with a strong backbone in the literature are used for performance comparison. The proposed architecture has been found to provide high segmentation accuracy (71.6% mIoU and 86.4% PA).
Açıklama
Anahtar Kelimeler
Deep learning, Histopathological image segmentation, Attention module, Convolution neural network
Kaynak
Biomedical Signal Processing and Control
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
80
