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强大的视觉变压器用于医疗图像分类

Joao Montrezol, Hugo S Oliveira, Jorge Araujo

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

视觉变压器 (ViT) 模型对医疗成像等复杂任务显示出希望. 新的混合规范化和增强技术在具有挑战性的数据集上提高了ViT性能和训练稳定性.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
医学成像分析医学成像分析

背景情况:

视觉转换器 (ViT) 架构提供了可扩展性和全球关注,推动了对计算机视觉的兴趣.
ViT的适应性使其适用于各种应用,但其在复杂,数据稀缺的医学成像任务上的性能需要改进.

研究的目的:

调查视觉变压器架构的性能边界.
开发新的技术来提高ViT在复杂任务上的性能,特别是医疗成像数据集.
为了应对诸如高可变性,阶级不平衡和医学成像中的有限样本大小等挑战.

主要方法:

提出了一套混合规范化和增强技术,适用于复杂的计算机视觉任务.
引入了一种新的损失函数,以提高模型训练的稳定性和性能.
开发了一种平滑可分化的激活功能,以增强训练动态.

主要成果:

拟议技术的整合显著改善了医疗成像数据集上的模型性能.
观察到加强培训趋同,表明更大的稳定性和效率.
证明了混合规范化和增强对具有挑战性的数据集的有效性.

结论:

开发的技术有效地提高视觉变压器在复杂的场景中的性能,特别是医学成像.
新的损失函数和激活函数有助于更稳定,更有效的模型训练.
这项工作为将ViT应用于高可变性,有限样本的医学成像任务提供了有价值的框架.

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