Histology segmentation using active learning on regions of interest in oral cavity squamous cell carcinoma

• Published in: Journal of pathology informatics Vol. 13; p. 100146
• Main Authors: Folmsbee, Jonathan, Zhang, Lei, Lu, Xulei, Rahman, Jawaria, Gentry, John, Conn, Brendan, Vered, Marilena, Roy, Paromita, Gupta, Ruta, Lin, Diana, Samankan, Shabnam, Dhorajiva, Pooja, Peter, Anu, Wang, Minhua, Israel, Anna, Brandwein-Weber, Margaret, Doyle, Scott
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2022; Elsevier
• Subjects: Active learning; Computational pathology; Digital pathology; Oral cavity cancer; Original; Region of interest; Semantic segmentation; U-net; Whole slide imaging; Digital pathology; Semantic segmentation; Region of interest; Active learning; U-net; Oral cavity cancer; Whole slide imaging; Computational pathology

In digital pathology, deep learning has been shown to have a wide range of applications, from cancer grading to segmenting structures like glomeruli. One of the main hurdles for digital pathology to be truly effective is the size of the dataset needed for generalization to address the spectrum of po...