Classification of Tissue Types in Histology Images Using Graph Convolutional Networks

Creative Commons License

Tepe E., Bilgin G.

2022 10th International Symposium on Digital Forensics and Security (ISDFS), İstanbul, Türkiye, 6 - 07 Haziran 2022, ss.1-3, (Tam Metin Bildiri)

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/isdfs55398.2022.9800776
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.1-3
  • İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet

Özet

This article uses Graphic Neural Network (GNN) models on histology images to classify tissue to find phenotypes. The majority of tissue phenotyping approaches are confined to tumor and stroma classification and necessitate a significant number of histology images. In this study, Graphics Convolutional Network (GCN) is used on the CRC Tissue Phenotyping dataset, which consists of seven tissue phenotypes, namely Benign, Complex Stroma, Debris, Inflammatory, Muscle, Stroma, and Tumor. First, the input images are converted into superpixels using the SLIC algorithm and the region neighborhood graphs (RAGs), where each superpixel is a node, and the edges connect neighboring superpixels to each other are converted. Finally, graphic classification is performed on the graphic data set using GCN.