JOINEDTrans:用于联合光盘/玻璃杯细分和孔检测的前导多任务变压器
Huaqing He1, Jiaming Qiu2, Li Lin3
1Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China; Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, Zhejiang, China.
Computers in biology and medicine
|May 23, 2024
概括
创新的深度学习框架JOINEDTrans通过联合细分光盘/光杯和检测光来增强视网膜里程碑分析,优于 fundus 图像上的现有方法.
科学领域:
- 眼科医生 眼科 眼科
- 医学图像分析 医学图像分析
- 计算机视觉 计算机视觉
背景情况:
- 深度学习模型已经进行了先进的视网膜里程碑分析 (光盘/杯,).
- 眼部病变和图像质量差的挑战自动细分和检测.
- 现有的方法经常单独解决地标,而不利用先前的信息.
研究的目的:
- 引入 JOINEDTrans,一个预先引导的多任务变压器框架,用于联合光盘/光杯细分和光检测.
- 提高视网膜基准分析对病变和图像质量问题的稳定性.
- 将空间先验,特别是船舶信息,纳入多任务学习框架.
主要方法:
- 一个具有细分和检测分支的多任务变压器框架 (JOINEDTrans).
- 通过预先训练在容器细分上的编码器将空间先验纳入.
- 一个两阶段的方法:粗细分/本地化,然后使用区域作物进行细微提炼.
主要成果:
- JOINEDTrans有效地结合了空间特征,以减轻病变和成像工件的扭曲.
- 与最先进的方法相比,该框架显示出更高的性能.
- 在GAMMA,REFUGE和PALM fundus图像数据集上进行了实验.
结论:
- JOINEDTrans提供了一个强大的,准确的解决方案,用于联合光盘/杯分段和膜检测.
- 整合空间前置显著提高了在具有挑战性的条件下性能.
- 拟议的方法在眼科中推进了视网膜标志的自动化分析.
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