Automated Labeling of Cancer Textures in Larynx Histopathology Slides Using Quasi-supervised Learning


Onder D., SARIOĞLU S., Karacali B.

ANALYTICAL AND QUANTITATIVE CYTOPATHOLOGY AND HISTOPATHOLOGY, cilt.36, sa.6, ss.314-323, 2014 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36 Sayı: 6
  • Basım Tarihi: 2014
  • Dergi Adı: ANALYTICAL AND QUANTITATIVE CYTOPATHOLOGY AND HISTOPATHOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.314-323
  • Anahtar Kelimeler: classification, histopathology, quasi-supervised learning, scatter matrices, statistical learning, texture classification
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

OBJECTIVE: To evaluate the performance of a quasisupervised statistical learning algorithm, operating on datasets having normal and neoplastic tissues, to identify larynx squamous cell carcinomas. Furthermore, cancer texture separability measures against normal tissues are to be developed and compared either for colorectal or larynx tissues.