Cross-View Self-Similarity Using Shared Dictionary Learning for Cervical Cancer Staging

• Published in: IEEE access Vol. 7; pp. 30079 - 30088
• Main Authors: Bnouni, Nesrine, Rekik, Islem, Rhim, Mohamed Salah, Essoukri Ben Amara, Najoua
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cancer; Cervical cancer; cervical cancer stage; Computer vision; cross-view; Dictionaries; Feature extraction; Learning; low-rank models; Machine learning; Magnetic resonance imaging; Medical imaging; Patches (structures); Self-similarity; Shared dictionary learning; Target recognition; Tumors

Dictionary Learning (DL) has gained large popularity in solving different computer vision and medical image problems. However, to the best of our knowledge, it has not been used for cervical tumor staging. More importantly, there have been very limited works on how to aggregate different interaction...