双任务协作优化,用于 fundus 图像疾病诊断和质量评估
Hao Liu1, Kanwei Wang1, Yuexin Luo1
1School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, Jiangsu, People's Republic of China.
Medical engineering & physics
|February 5, 2026
概括
这项研究引入了一种新型网络 (DTCONet),可以共同优化 fundus 图像质量评估和疾病诊断. 双任务方法通过利用图像质量和病理特征之间的关系来提高诊断准确性.
科学领域:
- 医疗图像分析 医学图像分析
- 眼科医生 眼科 眼科
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- Fundus 图像质量对于准确的疾病诊断至关重要.
- 诊断能力是评估底部图像质量 (FIQ) 的关键指标.
- 现有的方法往往独立地解决这些任务.
研究的目的:
- 提出一个双任务协作优化网络 (DTCONet),探索FIQ和疾病诊断之间的相互作用.
- 通过相互促进来提高这两项任务的执行力.
- 为FIQ评估和疾病诊断之间的关系提供新的见解.
主要方法:
- 开发了一种双分支特征提取框架,以捕获细结构和病理特征.
- 设计了一个双重任务模块,用于使用共享表示方式并行处理质量评估和疾病诊断.
- 引入了一个协作优化模块,以利用这两个任务之间的相关性.
主要成果:
- DTCONet在提高 fundus 图像质量评估和疾病诊断方面表现出有效性.
- 在五个数据集上的实验证实了模型的性能和通用性.
- 与独立任务模型相比,协作优化方法显示了显著的改进.
结论:
- 拟议的DTCONet有效地整合了质量评估和疾病诊断,以改善医疗图像分析.
- 该研究强调了FIQ和诊断性能之间的协同关系.
- 在临床环境中,DTCONet为推进自动化 fundus 图像分析提供了一个有前途的方法.
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