在不平衡的标签分配下,半监督的乳腺癌诊断的类意识的多层次注意力学习
Renjun Wen1, Yufei Ma2, Changdong Liu1
1China Comservice Enrising Information Technology Co., Ltd., Chengdu, Sichuan, 610041, China.
Medical & biological engineering & computing
|February 5, 2025
概括
这项研究引入了用于乳腺癌诊断的新型半监督深度学习模型,显著减少了对标记数据的需求. 该模型准确地识别了微观图像中的关键区域,提高了诊断效率.
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:
- 早期诊断乳腺癌对于患者的治疗结果至关重要.
- 深度学习模型可以提高诊断的准确性,但需要大量的标记数据.
- 现有的模型在特征提取和不平衡的数据集方面扎.
研究的目的:
- 为乳腺癌诊断开发一个半监督的深度学习模型.
- 减少对大量标记微观数据的依赖.
- 为了增强特征提取和解决乳腺癌分类中的类不平衡.
主要方法:
- 提出了一个类意识的多层次注意力学习模型.
- 开发了一个多层次的聚变注意模块,用于关键区域的识别.
- 实现了一个类意识的自适应性伪标签模块,以实现平衡的学习.
主要成果:
- 在BACH数据集上获得了86.7%的准确性.
- 仅使用40%标记的显微数据证明了高性能.
- 有效地减少了对大量数据注释的依赖.
结论:
- 拟议的模型为半监督乳腺癌诊断提供了一个有希望的方法.
- 该模型有效地处理有限的标记数据和不平衡的类分布.
- 这种方法可以减轻医学专业人员在乳腺癌查方面的负担.
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