Name
Abstract | ||
|---|---|---|
Goal: Most state-of-the-art computer-aided endoscopic diagnosis methods require pixelwise labeled data to train various supervised machine learning models. However, it is a tedious and time-consuming work to collect sufficient precisely labeled image data. Fortunately, we can easily obtain huge endoscopic medical reports including the diagnostic text and images, which can be considered as weakly l... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TBME.2016.2530141 | IEEE Transactions on Biomedical Engineering |
Keywords | Field | DocType |
Medical diagnostic imaging,Training,Measurement,Computational modeling,Lesions,Manuals | Computer vision,Pattern recognition,Feature mapping,Computer-aided,Computer science,Artificial intelligence,Labeled data | Journal |
Volume | Issue | ISSN |
63 | 11 | 0018-9294 |
Citations | PageRank | References |
4 | 0.38 | 42 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuai Wang | 1 | 24 | 2.98 |
| Yang Cong | 2 | 684 | 38.22 |
| Huijie Fan | 3 | 30 | 5.93 |
| Lianqing Liu | 4 | 44 | 24.68 |
| Xiaoqiu Li | 5 | 4 | 0.38 |
| Yunsheng Yang | 6 | 20 | 3.95 |
| Y. Tang | 7 | 243 | 33.69 |
| Huaici Zhao | 8 | 14 | 4.91 |
| Haibin Yu | 9 | 201 | 25.62 |