Name
Abstract | ||
|---|---|---|
Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks using a small amount of clean meta-data. Then the corrected masks can be used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model in an end-to-end way. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. It even achieves comparable performance with the model training on supervised data in some noisy settings. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/BIBM52615.2021.9669450 | BIBM |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiangbo Shi | 1 | 0 | 1.01 |
| Chang Jia | 2 | 0 | 0.34 |
| Zeyu Gao | 3 | 0 | 2.37 |
| Tieliang Gong | 4 | 2 | 4.75 |
| Chunbao Wang | 5 | 1 | 3.75 |
| Chen Li | 6 | 7 | 7.15 |