Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center.

By T. Freer, M. Ulissey. Radiology, 2001

854 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Oncology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/55f1596a7c8073d8dc675a3e86519fd5f9e1f5d8

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. Gary S. Collins
2. Fábio J. Ayres
3. Rangaraj Mandayam Rangayyan
4. J. E. Leo Desautels
5. A. Ramli
6. Shohreh Kasaei
7. John P. A. Ioannidis
8. Arnau Oliver

In Oncology

1. A. Bovik
2. Gary S. Collins
3. Karel G. M. Moons
4. Kunio Doi
5. Ronald A. Castellino

In Pulmonary and Respiratory Medicine

1. Gary S. Collins
2. Karel G. M. Moons
3. Kunio Doi
4. Ronald A. Castellino
5. B. van Ginneken
6. Ronald M. Summers
7. Shijun Wang

Citations in the corpus

• A review of automatic mass detection and segmentation in mammographic images (2010, methodology): “In 2001 Freer and Ulissey (2001), using a database containing 12,860 patients, concluded that the use of CAD in the interpretation of screening mammograms can increase the detection of early-stage malignancies.”
• Current status and future potential of computer-aided diagnosis in medical imaging. (2005, background): “It has also been reported that CAD has provided a gain of approximately 20% in the early detection of breast cancers on mammograms [29].”
• Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness (2020, background): “1 81 However, almost 20 years after the FDA’s first license for a mammography based CAD system, national registry based studies have shown no significant improvement in diagnostic accuracy associated with CAD use in mammography interpretation.”
• A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs (2007, background): “Freer and Ulissey [19] performed a prospective study of the effect of CAD in screening, where 12,860 screening mammograms were interpreted with the help of a CAD system over a 12-month period.”
• Benign and malignant breast tumors classification based on region growing and CNN segmentation (2015, background): “Computer aided diagnosis (CAD) systems are sets of automatic or semi-automatic tools using computer technology to help radiologists with detection and classification of breast abnormalities (Freer & Ulissey, 2001).”
• Computer-Aided Detection and Diagnosis in Mammography (2005, background): “39 FPI on masses (both spiculated and non-spiculated together) [90].”