Name
Title | ||
|---|---|---|
Requirement of microcalcification detection for computerized classification of malignant and benign clustered microcalcifications |
Abstract | ||
|---|---|---|
We are developing computerized schemes to detect clustered microcalcifications in mammograms, and to classify malignant versus benign microcalcifications. The purpose of this study is to investigate the effects on the performance of computer classification when results of computer-detected true microcalcifications and computer detected false-positive signals are used as input to the computer classification scheme. We found that when trained using manually identified microcalcifications, the computer classification performance was not degraded significantly when up to 60% of true microcalcifications were missed, and when false-positive signals made up approximately one half of the computer detection. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1117/12.310907 | PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE) |
Keywords | Field | DocType |
computer-aided diagnosis (CAD),artificial neural networks (ANN),classification,breast cancer,mammography,microcalcification | Nuclear medicine,Mammography,Microcalcification,Pattern recognition,Computer science,Classification scheme,Artificial intelligence,Computing systems | Conference |
Volume | ISSN | Citations |
3338 | 0277-786X | 3 |
PageRank | References | Authors |
0.63 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yulei Jiang | 1 | 80 | 8.90 |
| Robert M Nishikawa | 2 | 599 | 58.25 |
| john papaioannou | 3 | 7 | 4.34 |