乳房超声波图像的多类分类使用视觉转换器为基础的集体学习
Tuğçe Taşar Yıldırım1, Orhan Yaman2, İrfan Kılıç3
1Department of Internal Medicine, Fethi Sekin City Hospital, Elazig 23300, Türkiye.
Diagnostics (Basel, Switzerland)
|September 13, 2025
概括
一个新的视觉变压器 (ViT) 组合模型准确地分类乳房超声波图像. 这种人工智能方法可以提高正常,良性和恶性瘤的诊断准确度,帮助临床决策.
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:
- 乳房超声波成像对于疾病检测至关重要.
- 正确分类正常,良性和恶性乳腺病变是必不可少的.
- 现有的方法可能在准确性和可解释性方面存在局限性.
研究的目的:
- 开发基于视觉变压器 (ViT) 的集体架构,用于分类乳房超声波图像.
- 提高自动化乳腺损伤分类的准确性和可靠性.
- 评估拟议模型在乳腺超声波图像 (BUSI) 数据集上的性能.
主要方法:
- 利用乳房超声波图像 (BUSI) 数据集,包括133个正常图像,437个良性图像和210个恶性图像.
- 应用兴趣区域 (ROI) 分段和图像增强用于集中分析.
- 采用了三种ViT模型 (ViT-Base,DeiT,ViT-Small) 来进行特征提取,其次是多层感知子 (MLP) 分类器.
- 实施了10倍分层交叉验证,以进行可靠的模型培训和评估.
主要成果:
- 在良性 (精度96.2%,回忆率86.3%) 和恶性 (精度76.4%,回忆率92.9%) 类中实现了高精度和回忆.
- 达到100%的成功率正常类.
- 报告的曲线下面面积 (AUC) 值为良性类的0.97,恶性类的0.96和正常类的1.00.
结论:
- 基于ROI的ViT + MLP + Ensemble架构比传统的卷积神经网络 (CNN) 方法提供了更高的准确性和可解释性.
- 表现稳定,特别是在少数群体中,突出了其在临床决策支持方面的潜力.
- 介绍了用于乳房超声波图像分类的可靠和灵活的AI解决方案.
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