Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features

By Scott Doyle, S. Agner, Anant Madabhushi, M. Feldman, John Tomaszewski. IEEE International Symposium on Biomedical Imaging, 2008

331 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/22338e22870c6e3aa5c034ac3a7a3ac0353b03f3

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

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Breast Cancer Histopathology Image Analysis: A Review (2014, neutral): “ture in combination with nuclear features have been shown to be successful in distinguishing between different breast cancer grades [84], [107].”
Breast cancer histopathological image classification using Convolutional Neural Networks (2016, neutral): “are focused on Whole-Slide Imaging (WSI) [7], [8], [6], [4], [9].”
Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks (2014, methodology): “Doyle et al. 6 used the graph-based features, 15 statistics gray level features, 16 second order statistical features, 64 Gabor ﬁlter features and 24 nuclear features per each of three color channels in HSI color space using three diﬀerent windows sizes.”
Identification of tumor epithelium and stroma in tissue microarrays using texture analysis (2012, methodology): “For classifying breast histology images, texture-based operators using supervised learning have been employed [34,35].”
Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles (2016, methodology): “…but many of these methods selected smaller areas of the tumor for processing (Alexe et al., 2009; Adiga et al., 2006; Aiad et al., 2009; Al-Kadi, 2010; Altunbay et al., 2010; Baak et al., 1981; Basavanhally et al., 2010, 2008; Brook et al., 2007; Doyle et al., 2012a, 2012b, 2008, 2007; Fig.”
Histology image analysis for carcinoma detection and grading (2012, methodology): “…study has been extensively employed for cancer detection and grading applications, including prostate [41,42,105,106,108], breast [6,43,109], cervix [56,57,79,122], and lung [75,78,87,131] cancer grading, neuroblastoma categorization [61,81,83,84], and follicular lymphoma grading…”
Deep features for breast cancer histopathological image classification (2017, background): “It is worth mentioning that most of these recent works related to BC classification are focused on Whole-Slide Imaging (WSI) [15]– [18].”