A-Eval:用于跨数据集和跨模式评估腹部多器官细分的基准
Ziyan Huang1, Zhongying Deng2, Jin Ye3
1Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200000, China.
Medical image analysis
|February 19, 2025
概括
对于腹部器官细分的深度学习模型与一般化作斗争. 一个新的基准,A-Eval,显示大数据集有所改善,但没有解决跨模式和跨数据集性能问题.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 对于腹部多器官细分的深度学习模型,由于有限的特定训练数据,面临着泛化挑战.
- 新兴的大规模数据集提供了潜力,但也引发了关于跨数据集和跨模式概括性的问题.
- 强大的腹部细分模型对于临床应用至关重要.
研究的目的:
- 引入A-Eval,用于评估不同数据集和成像方式 (CT和MRI) 的腹部多器官细分模型的基准.
- 评估在大规模数据上训练的深度学习模型的通用性.
- 确定改善医疗图像细分中的模型通用性的策略.
主要方法:
- 开发了A-Eval,整合了七个腹部数据集,涵盖CT和MRI模式.
- 评估了深度学习模型在跨数据集和跨模式任务上的性能.
- 研究了跨多个数据集和模式的联合培训的影响.
主要成果:
- 在腹部多器官细分中仍然存在显著的域差距,即使使用大型数据集.
- 虽然增加数据集大小可以提高概括性,但对未见数据的模型性能仍然不一致.
- 跨数据集和模式的联合培训提高了概括性,但由于注释不一致而受到挑战.
结论:
- 目前的大规模数据集本身不足以保证腹部细分模型的强大概括.
- 跨不同临床场景和模式的多样化,精心策划的培训数据对于开发可靠的医学成像AI至关重要.
- 需要进一步的研究来解决域间隙和注释不一致性,以改善模型通用性.
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