研究建立一个基于转移学习的乳房质超声波图像的四个分类模型的研究
Jinlao Li1, Bo Zhao2, YongQi Chen1
1Diagnostic Ultrasound Center, The First Hospital of Jilin University, Changchun, China.
PloS one
|January 6, 2026
概括
优化的深度学习模型在超声波中准确地分类乳腺质量,优于专家放射科医生. 这种人工智能方法有助于早期诊断乳腺癌.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 通过超声波对乳房质量进行分类对于早期诊断至关重要,但由于类似的视觉特征而具有挑战性.
- 深度卷积神经网络 (DCNNs) 显示出图像分析的潜力,但需要优化用于医疗应用.
研究的目的:
- 在超声波图像中使用DCNN来评估优化转移学习 (TL) 来分类四种类型的乳腺瘤 (侵入性导管癌,纤维腺瘤,粘膜癌,炎症质).
- 将这些人工智能模型的性能与经验丰富的放射科医生进行比较.
主要方法:
- 利用了来自294名患者的346张超声波图像的数据集,应用了广泛的数据增强.
- 使用五个预训练的DCNN模型 (ResNet18,ResNet50,DenseNet121,MobileNetV2,GoogLeNet) 实现转移学习.
- 为四类分类优化了超参数,并对两名高级放射科医生进行了绩效指标评估.
主要成果:
- 丹塞Net121和谷歌LeNet模型实现了高精度 (分别为0.912和0.926),明显超过了放射科医生的共识 (0.691).
- 经过优化后的模型显示了侵入性导管癌,粘膜癌和炎症质量分类的准确性提高.
- 统计分析证实了顶级DCNN模型在人类专家的表现优越.
结论:
- 使用DCNNs,特别是DenseNet121和GoogLeNet的优化转移学习,可从超声波图像中提供高度准确的乳腺质量分类.
- 这种人工智能驱动的方法显著优于专家放射科医生,为诊断乳腺癌的临床决策支持提供了一个有希望的工具.
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