ADAM-Net:用于关节MG细分和MGD分级的解剖学引导的注意力无监督域调整
Junbin Fang1,2,3,4, Xuan He1,2,3,4, You Jiang1,2,3,4
1Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China.
Journal of imaging
|January 27, 2026
概括
ADAM-Net是一个新的深度学习框架,有效地解决了多中心成像中自动化梅博姆腺功能障碍 (MGD) 评估的领域转移. 它通过联合执行腺体细分和分类来实现高精度的MGD分类.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 梅博米腺功能障碍 (MGD) 是干眼疾病的主要原因.
- 深度学习 (DL) 已经改善了MGD评估,但在不同的成像设备上与域移动作斗争.
- 现有的DL方法通常将MG细分和MGD分类作为单独的任务来处理.
研究的目的:
- 为自动化MGD评估开发一个强大的,多任务的深度学习框架.
- 为应对多中心医学成像数据集领域转移的挑战.
- 为了共同建模梅博米腺细分和MGD分类.
主要方法:
- 提出了ADAM-Net,一个以注意力为导向的无监督域调整多任务框架.
- 引入了结构意识的多任务学习和解剖学引导的注意力机制.
- 在MGD-1K→{K5M,CR-2,LV II}数据集上评估了跨领域的性能.
主要成果:
- 在目标域中,ADAM-Net的分类准确率达到77.93%,74.86%和81.77%.
- 显著优于主流的无监督域调整 (UDA) 方法.
- 证明了强大的区分能力和跨域特征对齐,通过F1分数,MCC分数和t-SNE可视化进行验证.
结论:
- 在多中心场景中,ADAM-Net为MGD自动评估提供了有效和强大的解决方案.
- 该框架表现出强大的解释性,并解决领域转移的挑战.
- 联合建模细分和分类可以提高性能和稳定性.
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