在乳腺损伤分类和诊断中应用YOLO-v7和YOLO-v8转移学习模型
Kaiting Jiang1,2, Yuegui Wang1, Haiquan He1,2
1Department of Ultrasound, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou 363005, China.
Current medical imaging
|February 15, 2026
概括
两个人工智能模型YOLO-v7和YOLO-v8在超声波图像中有效地分类了乳腺病变. YOLO-v8表现出卓越的性能和通用性,超过了高级放射科医生,并显示出临床使用的潜力.
科学领域:
- 医疗成像中的人工智能
- 机器学习用于诊断支持
- 乳腺癌损伤分类 乳腺癌损伤分类
背景情况:
- 准确的乳腺癌病变评估对于早期检测至关重要.
- 超声波的诊断性能受到图像复杂性和操作员可变性的限制.
- 提高准确性和减少操作员依赖是临床的关键挑战.
研究的目的:
- 评估YOLO-v7和YOLO-v8模型在超声波图像上的乳腺病变分类方面的性能.
- 将这些AI模型的诊断准确性与人类放射科医生进行比较.
- 评估AI在协助乳腺超声波临床决策方面的潜力.
主要方法:
- 追溯分析了7025张乳房超声波图像.
- 使用转移学习,数据增强和类平衡来训练YOLO-v7和YOLO-v8模型.
- 在内部和外部测试集上的性能评估,包括与读者研究的比较.
主要成果:
- 与YOLO-v7.7相比,YOLO-v8在外部测试组上获得了更高的准确性,回忆力,特异性,精度和F1分数.
- 无论是YOLO-v7还是YOLO-v8,在准确性,特异性和精度方面都明显超过了高级放射科医生.
- YOLO-v8展示了卓越的诊断效率和概括能力.
结论:
- 由于其先进的架构和性能,YOLO-v8在乳腺病变分类中具有很强的临床应用潜力.
- 像YOLO-v8这样的AI模型可以接近或超过经验丰富的放射科医生的诊断性能.
- 这些人工智能工具可以帮助不那么有经验的读者,提高诊断一致性和准确性.
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