Name
Abstract | ||
|---|---|---|
Nowadays, breast cancer is the leading cause of cancer death for women all over the world. Since the reason of breast cancer is unknown, early detection of the disease plays an important role in cancer control, saving lives and reducing costs. Among different modalities, automated 3-D breast ultrasound (3-D ABUS) is a new and effective imaging modality which has attracted a lot of interest as an adjunct to mammography for women with dense breasts. However, reading ABUS images is time consuming for radiologists and subtle abnormalities may be overlooked. Hence, computer aided detection (CADe) systems can be utilized as a second interpreter to assist radiologists to increase their screening speed and sensitivity. In this paper, a general architecture representing different CADe systems for ABUS images is introduced and the approaches for implementation of each block are categorized and reviewed. In addition, the limitations of these systems are discussed and their performance in terms of sensitivity and number of false positives per volume are compared. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s10462-019-09722-7 | Artificial Intelligence Review |
Keywords | Field | DocType |
Ultrasound, Breast, Detection, Mass | Modalities,Breast ultrasound,Mammography,Early detection,Breast cancer,Computer science,Computer aided detection,Medical physics,Artificial intelligence,Cancer,Machine learning,False positive paradox | Journal |
Volume | Issue | ISSN |
53 | 3 | 0269-2821 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ehsan Kozegar | 1 | 2 | 1.37 |
| soryani | 2 | 29 | 9.48 |
| Hamid Behnam | 3 | 43 | 9.94 |
| Masoumeh Salamati | 4 | 0 | 0.34 |
| Tao Tan | 5 | 46 | 10.25 |