Name
Abstract | ||
|---|---|---|
Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for brea... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/JBHI.2017.2731873 | IEEE Journal of Biomedical and Health Informatics |
Keywords | Field | DocType |
Lesions,Fractals,Ultrasonic imaging,Imaging,Filtering,Breast cancer | Breast ultrasound,Computer vision,Ranking,Pattern recognition,Convolutional neural network,Computer science,Transfer of learning,Filter (signal processing),Artificial intelligence,Deep learning,Ultrasound,False positive paradox | Journal |
Volume | Issue | ISSN |
22 | 4 | 2168-2194 |
Citations | PageRank | References |
27 | 0.94 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Moi Hoon Yap | 1 | 190 | 27.82 |
| Gerard Pons | 2 | 40 | 3.16 |
| Joan Martí | 3 | 246 | 10.91 |
| Sergi Ganau | 4 | 50 | 3.88 |
| Melcior Sentís | 5 | 85 | 5.51 |
| Reyer Zwiggelaar | 6 | 711 | 103.74 |
| Adrian K. Davison | 7 | 99 | 6.51 |
| Robert Martí | 8 | 324 | 45.26 |