Name
Title | ||
|---|---|---|
Automatic Estimation of Ulcerative Colitis Severity by Learning to Rank With Calibration |
Abstract | ||
|---|---|---|
For automatic disease-severity-level estimation, a large-scale medical image dataset with level annotations is generally necessary. However, attaching absolute-level annotations (such as levels 0, 1, and 3) is very costly and even inaccurate due to the level ambiguity. In this study, we proved experimentally that using a ranking function for level estimation can relax this difficulty. We propose a multi-task learning method for automatically estimating disease-severity levels that combine learning to rank with regression. The ranking function of the proposed method is trainable by relative-level and a small number of absolute-level annotations. For relative-level annotation, an annotator only needs to specify that one image has a higher disease level than another-this is much easier than absolute-level annotation. The proposed method enables disease-severity classification by calibrating the ranking function based on relative-level annotation through regression. The effectiveness of the method was proved through a large-scale experiment of ulcerative colitis-severity estimation with colonoscopy images. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ACCESS.2022.3155769 | IEEE ACCESS |
Keywords | DocType | Volume |
Annotations, Diseases, Multitasking, Estimation, Medical diagnostic imaging, Task analysis, Convolutional neural networks, Computer-aided diagnosis, deep learning, endoscopic image dataset, learning to rank, relative-level annotation | Journal | 10 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takeaki Kadota | 1 | 0 | 0.34 |
| Kentaro Abe | 2 | 0 | 0.34 |
| Ryoma Bise | 3 | 137 | 16.83 |
| Takuji Kawamura | 4 | 0 | 0.34 |
| Naokuni Sakiyama | 5 | 0 | 0.34 |
| Kiyohito Tanaka | 6 | 0 | 2.37 |
| Seiichi Uchida | 7 | 790 | 105.59 |