A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications

By Liyang Wei, Yongyi Yang, Robert M. Nishikawa, Yulei Jiang. IEEE Transactions on Medical Imaging, 2005

318 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ea7685f5ee65c4c152478111b5bfcb23a446d358

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

1. Paul Sajda
2. Ronald M. Summers
3. Shijun Wang

In Radiology, Nuclear Medicine and Imaging

1. Paul Sajda
2. Temesguen Messay
3. Ronald M. Summers
4. Russell C. Hardie
5. Steven K. Rogers
6. Shijun Wang

Citations in the corpus

• Machine Learning and Radiology (2012, methodology): “SVMs perform well in detection of microcalcifications on mammography CAD (El-Naqa et al., 2002; Tang et al., 2009; Wei et al., 2005b).”
• A new computationally efficient CAD system for pulmonary nodule detection in CT imagery (2010, background): “(2005) consider various classifier approaches including a support vector machine, kernel Fisher discriminant, relevance vector machine, and committee machines (ensemble averaging and AdaBoost) for automated classification of clustered microcalcifications (Wei et al., 2005).”
• Machine learning for detection and diagnosis of disease. (2006, background): “ANRV281-BE08-08 ARI 7 April 2006 14:57 Biomarkers: anatomic, physiologic, biochemical, or molecular parameters associated with the presence and severity of specific disease states classification and regression functions, for example, constructed by ANNs.”