Name
Abstract | ||
|---|---|---|
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%. Due to subtle texture changes of PDAC, pancreatic dual-phase imaging is recommended for better diagnosis of pancreatic disease. In this study, we aim at enhancing PDAC automatic segmentation by integrating multi-phase information (i.e., arterial phase and venous phase). To this end, we present Hyper-Pairing Network (HPN), a 3D fully convolution neural network which effectively integrates information from different phases. The proposed approach consists of a dual path network where the two parallel streams are interconnected with hyper-connections for intensive information exchange. Additionally, a pairing loss is added to encourage the commonality between high-level feature representations of different phases. Compared to prior arts which use single phase data, HPN reports a significant improvement up to 7.73% (from 56.21% to 63.94%) in terms of DSC. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-32245-8_18 | Lecture Notes in Computer Science |
DocType | Volume | ISSN |
Conference | 11765 | 0302-9743 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuyin Zhou | 1 | 97 | 10.94 |
| Yingwei Li | 2 | 7 | 6.35 |
| Zhishuai Zhang | 3 | 95 | 5.36 |
| Yan Wang | 4 | 134 | 11.13 |
| Angtian Wang | 5 | 4 | 1.39 |
| Elliot K. Fishman | 6 | 164 | 27.51 |
| Alan L. Yuille | 7 | 10339 | 1902.01 |
| Seyoun Park | 8 | 53 | 4.88 |