Name
Title | ||
|---|---|---|
Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation. |
Abstract | ||
|---|---|---|
Automated whole breast ultrasound (ABUS) has been widely used as a screening modality for examination of breast abnormalities. Reviewing hundreds of slices produced by ABUS, however, is time consuming. Therefore, in this paper, a fast and effective computer-aided detection system based on 3-D convolutional neural networks (CNNs) and prioritized candidate aggregation is proposed to accelerate this ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TMI.2018.2860257 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Lesions,Feature extraction,Ultrasonic imaging,Image edge detection,Breast cancer | Breast ultrasound,Computer vision,Automated whole-breast ultrasound,Sliding window protocol,Pattern recognition,Convolutional neural network,Feature extraction,Artificial intelligence,Ultrasonic imaging,Mathematics,False positive paradox,Test set | Journal |
Volume | Issue | ISSN |
38 | 1 | 0278-0062 |
Citations | PageRank | References |
5 | 0.42 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tsung-Chen Chiang | 1 | 5 | 0.42 |
| Yao-Sian Huang | 2 | 10 | 1.61 |
| Rong-Tai Chen | 3 | 19 | 1.56 |
| Chiun-Sheng Huang | 4 | 153 | 9.33 |
| Ruey-Feng Chang | 5 | 395 | 34.88 |