A Deep Learning Method for Pneumoconiosis Staging on Chest X-Ray Under Label Noise
The ambiguous properties of small opacities in pneumoconiosis chest radiographs can cause diagnostic drift, which in turn leads to the presence of noisy labels in the datasets collected from hospitals that can negatively impact the generalization of deep learning models. To tackle this issue, we pro...
Saved in:
| Main Authors: | Wenjian Sun, Dongsheng Wu, Jiang Shen, Yang Luo, Hao Wang, Li Min, Chunbo Luo |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11086579/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Tripartite: Tackling Realistic Noisy Labels with More Precise Partitions
by: Lida Yu, et al.
Published: (2025-05-01) -
Dual-Stage Clean-Sample Selection for Incremental Noisy Label Learning
by: Jianyang Li, et al.
Published: (2025-07-01) -
IUR-Net: A Multi-Stage Framework for Label Refinement Tasks in Noisy Remote Sensing Samples
by: Yibing Xiong, et al.
Published: (2025-06-01) -
Survival analysis of patients with pneumoconiosis followed in occupational medicine clinics: 10 years experience
by: Melike Yüksel Yavuz, et al.
Published: (2025-05-01) -
The New Nutrition Facts Label
by: Samantha Buddemeyer, et al.
Published: (2018-01-01)