Nuclear feature extraction for breast tumor diagnosis

By W. Street, William H. Wolberg, Olvi L. Mangasarian. 1993

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Citations in the corpus

Smoothed log-concave maximum likelihood estimation with applications (2011, background): “Stroud (1971) gives a brief introduction to the problem of evaluating integrals over the unit simplex, while Grundmann and Möller (1978) proposed a combinatorial method.”
Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential (2014, background): “Earliest works in the field date back to the early 90s [6]–[8] but are in relatively small number, presumably due to the limited penetration of digital equipment in pathology.”
ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap (2015, methodology): “ClustVis includes multiple popular public data sets for testing purposes: NKI breast cancer data set (14,15), Wisconsin diagnostic breast cancer data set (16) and Fisher’s Iris data set (17).”
A hybrid approach of neural network and memory-based learning to data mining (2000, methodology): “The total number of instances is 392, which we randomly divided into 300 training data and 92 testing data for each trial.WDBC is a classification problem on the breast cancer diagnostic data [ 27 ].”
Visualizing Data using t-SNE (2008, methodology): “Cell nuclei that are relevant to breast cancer, for example, are described by approximately 30 variables (Street et al., 1993), whereas the pixel intensity vectors used to represent images or the word-count vectors used to represent documents typically have thousands of dimensions.”
Vertical Federated Learning: Concepts, Advances, and Challenges (2022, background): “Dataset Data Type Size Description Income [140] Tabular 48842 Demographics and income features Bank [141] Tabular 41188 Demographics and economic features Credit Card [142] Tabular 30000 Demographics and payments Give Me Some Credit [143] Tabular 250000 Debt features MIMIC III [144] Tabular 42276 Me”
Inference and Modeling with Log-concave Distributions (2009, methodology): “They apply the log-concave EM algorithm to the Wisconsin breast cancer data of Street et al. (1993) and obtain only 121 misclassified instances compared to 144 with the Gaussian EM algorithm.”