Multifeature Prostate Cancer Diagnosis and Gleason Grading of Histological Images

By Ali Tabesh, Mikhail Teverovskiy, Ho-Yuen Pang, Vinay P. Kumar, David Verbel, Angeliki Kotsianti, Olivier Saidi. IEEE Transactions on Medical Imaging, 2007

384 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/032c61231e22a8066b43aec89d4ea65a8d782a10

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Weakly supervised histopathology cancer image segmentation and classification (2014, methodology): “(2010) to detect and grade colon cancer in histopathology images; multiple features including color, texture, and morphologic cues at the global and histological object levels were adopted in prostate cancer detection (Tabesh et al., 2007); Boucheron et al.”
DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation (2016, methodology): “For this kind of method, various hand-crafted features including texture, color, morphological cues and Haar-like features were utilized to detect the glandular structure from histology images [11, 38, 13, 36, 37, 28, 23, 32]; (2) structure based methods.”
Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology (2016, background): “These features were previously found to be useful for predicting prostate cancer aggressiveness on pathology [47].”
Histopathological Image Analysis: A Review (2009, neutral): “[117] found 96.”
Gland segmentation in colon histology images: The glas challenge contest (2016, background): “It is true that there are a number of successful works that follow such an approach [Jafari-Khouzani and Soltanian-Zadeh, 2003, Tabesh et al., 2007, Altunbay et al., 2010, Basavanhally et al., 2010, Ozdemir and Gunduz-Demir, 2013, Gultekin et al., 2015].”
Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles (2016, background): “…Sboner et al., 2003; Schnorrenberg et al., 1997; Sertel et al., 2010; Sparks and Madabhushi, 2013; Sudbø et al., 2000; Tabesh and Teverovskiy, 2006; Tabesh et al., 2007, 2005; Tahir and Bouridane, 2006; Tasoulis et al., 2003; Teverovskiy et al., 2004; Thiran and Macq, 1996; Tsai et al., 2009; Xu…”
Automatic Classification for Pathological Prostate Images Based on Fractal Analysis (2009, methodology): “[32]–[34] proposed an automatic two-stage system for prostate cancer diagnosis and Gleason grading.”