Name
Abstract | ||
|---|---|---|
Accurate quantification of optic disc (OD) is clinically significant for the assessment and diagnosis of ophthalmic disease. Multi-index OD quantification, i.e., to simultaneously quantify a set of clinical indices including 2 vertical diameters (cup and disc), 2 whole areas (disc and rim), and 16 regional areas, is an untouched challenge due to its complexity of the multi-dimensional nonlinear mapping and various visual appearance across patients. In this paper, we propose a novel multi-task ensemble learning framework (DMTFs) to automatically achieve accurate multi-types multi-index OD quantification. DMTFs creates an ensemble of multiple OD quantification tasks (OD segmentation and indices estimation) that are individually accurate and mutually complementary, and then learns the ensemble under a multi-task learning framework which is formed as a tree structure with a root network for shared feature representation, two branches for task-specific prediction, and a multitask ensemble module for aggregation of multi-index OD quantification. DMTFs models the consistency correlation between OD segmentation and indices estimation tasks to conform to the accurate multi-index OD quantification. Experiments on the ORIGA datasets show that the proposed method achieves impressive performance with the average mean absolute error on 20 indices of 0.99 +/- 0.20, 0.73 +/- 0.14 and 1.23 +/- 0.24 for diameters, whole areas and regional area, respectively. Besides, the obtained quantitative indices achieve competitive performance (AUC= 0.8623) on glaucoma diagnosis. As the first multi-index OD quantification, the proposed DMTFs demonstrates great potential in clinical application. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-32239-7_3 | Lecture Notes in Computer Science |
DocType | Volume | ISSN |
Conference | 11764 | 0302-9743 |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rongchang Zhao | 1 | 9 | 3.81 |
| Zailiang Chen | 2 | 1 | 1.02 |
| Liu Xiyao | 3 | 36 | 6.76 |
| Beiji Zou | 4 | 231 | 41.61 |
| Shuo Li | 5 | 887 | 72.47 |