Computer-Aided Detection and Diagnosis in Mammography

By Mehul P. Sampat, Mia K. Markey, A. Bovik. 2005

339 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/509bed5ac7709e04b6c9a309e679a881bcd674d3

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. A. Ramli
2. Issam El-Naqa
3. Jun Xu
4. Rangaraj Mandayam Rangayyan
5. Georgia D. Tourassi
6. Maciej A. Mazurowski
7. Syamsiah Mashohor
8. M. Iqbal Bin Saripan

In Pulmonary and Respiratory Medicine

1. Maciej A. Mazurowski

In Radiology, Nuclear Medicine and Imaging

1. Rangaraj Mandayam Rangayyan
2. Maciej A. Mazurowski
3. Xiahai Zhuang

Citations in the corpus

• Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. (2013, background): “The most important stages of mass detection algorithms include detection of suspicious regions and classification of suspicious area as normal tissues or masses [30].”
• Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances (2009, methodology): “Thesecond approach for mass detection is region-based [4].”
• Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance (2008, background): “Some comprehensive reviews on the topic can be found in Kawamoto, Houlihan, Balas, and Lobach (2005), Lisboa and Taktak (2006) and Sampat, Markey, and Bovik (2005).”