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MAFNet:一种用于细粒度分类糖尿病视网膜病变的新型适应性多尺度模型
Lei Sun1, Qinghua Peng1, Xiaoxia Xiao2
1School of Informatics, Hunan University of Chinese Medicine, 300 Xueshi Road, Hanpu Science and Education Park, Yuelu District, Changsha, 410208, Hunan Province, China.
Scientific reports
|September 1, 2025
概括
一个新的深度学习模型MAFNet通过有效地融合多尺度特征和建模病变关系来改善糖尿病视网膜病变的分级. 这种先进的方法提高了糖尿病视网膜病变的早期检测和临床干预.
科学领域:
- 眼科 眼科
- 医学成像
- 人工智能
背景情况:
- 糖尿病视网膜病 (DR) 是全球首要的失明原因.
- 早期发现和准确分类DR对于有效的临床干预至关重要.
- 现有的方法在多尺度病变特征融合和关系建模方面存在局限性.
研究的目的:
- 为细粒度糖尿病视网膜病变分级开发一种新的适应性多尺度卷积神经网络.
- 解决当前DR检测方法中特征融合和损伤关系建模的局限性.
主要方法:
- 拟议的MAFNet (多尺度自适应细粒度网络) 有三个核心模块:层次全球上下文模块 (HGCM),多尺度自适应注意模块 (MSAM) 和关系式多头注意模块 (RMA).
- HGCM使用多尺度聚合和动态融合扩展受体场,用于微小到大规模的病变特征.
- MSAM采用适应性注意力进行动态特征加权,增强关键病变的表现.
- 为了更好地识别细粒度,RMA同时使用多头关注模型特征关系.
- 实施多任务学习框架 (回归和分类),以捕捉DR的进展.
主要成果:
- MAFNet获得了高二次加权的卡帕值:DDR为0.934,Messidor-2为0.917和APTOS为0.936.
- 与Lanet和MPLNet等现有方法相比,显著提高了性能.
- 在三个公开数据集上进行验证, 证实了强大的性能.
结论:
- MAFNet在糖尿病视网膜病变的自动分级方面取得了重大进展.
- 该模型能够整合多个尺度的特征和模型损伤关系,从而提高准确性.
- MAFNet对糖尿病视网膜病变的早期和准确检测具有相当大的应用价值.
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