应用YOLOv6作为一个集体联合学习框架来分类乳腺癌病理图像
Chhaya Gupta1, Nasib Singh Gill1, Preeti Gulia1
1Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, India.
Scientific reports
|January 30, 2025
概括
通过修剪YOLOv6模型的联合学习 (FedL) 提高了乳腺癌检测的准确性. 这种保护隐私的方法在病理数据集上实现了高性能,优于传统方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 乳腺癌是女性死亡的主要原因,因此早期检测至关重要.
- 手动查方法可能导致诊断和治疗延迟,增加死亡风险.
- 乳腺扫描对于早期乳腺癌查至关重要,但提高诊断准确性和通用性仍然是一个挑战.
研究的目的:
- 通过使用乳房图像来研究联合学习 (FedL) 对乳腺癌检测的有效性.
- 为了比较FedL与传统的集中式培训方法的表现.
- 引入一个与FedL和同型加密集成的新型修剪YOLOv6模型,以提高隐私和准确性.
主要方法:
- 使用YOLOv6模型实现联合学习 (FedL) 进行乳腺癌检测.
- 开发一种新的同型加密和解密算法,以确保数据隐私.
- 使用BreakHis和BUSI乳腺癌病理学数据集对修剪过的YOLOv6模型进行培训和评估.
主要成果:
- 采用FedL进行修剪的YOLOv6模型在BreakHis数据集上达到98%的验证准确度,在BUSI数据集上达到97%.
- 联合学习 (FedL) 证明了可行性,培训高质量的模型,使用最小的沟通轮.
- 经过 FedL 训练的 YOLOv6 模型的性能优于 VGG-19,ResNet-50 和 InceptionV3.3 等已建立的算法.
结论:
- 联合学习 (FedL) 是一种可行且有效的方法,用于开发准确的乳腺癌检测模型,同时保持数据隐私.
- 用FedL修剪的新型YOLOv6模型在区分良性和恶性乳腺组织方面取得了重大进展.
- 这项研究强调了FedL在改善AI驱动的诊断工具在医学成像中的通用性和性能方面的潜力.
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