通过对比学习来诊断皮肤病变的一致表示
Zizhou Wang1, Lei Zhang2, Xin Shu2
1College of Computer Science, Sichuan University, Chengdu 610065, China; Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore.
Computer methods and programs in biomedicine
|October 14, 2023
概括
人工智能通过解脱多模式数据来帮助皮肤病变诊断,克服模式偏差以提高准确性. 这种基于对比的方法可以提高早期检测诸如黑色素瘤之类的疾病.
科学领域:
- 皮肤病学和人工智能研究
- 医学图像分析 医学图像分析
- 机器学习用于医疗保健
背景情况:
- 皮肤病变,包括黑色素瘤,是常见的,需要早期检测.
- 人工智能 (AI) 在早期皮肤病变检测方面表现有前途.
- 在临床环境中整合多模式人工智能面临挑战,包括忽视模式偏差.
研究的目的:
- 引入一种多模特特征学习技术用于皮肤病诊断.
- 在人工智能驱动的皮肤病变分析中解决和减轻模式偏差.
- 开发一种方法,在不同的数据模式中实现一致的表示学习.
主要方法:
- 开发了一种新的技术,即基于对比的一致表示解.
- 敌对域调整被用来解开特征并创建共享的表示.
- 采用对比学习来确保跨模式的常见损伤属性的统一性.
主要成果:
- 拟议的技术在多重分类任务中实现了76.1%的平均准确率.
- 它在性能上超越了现有的最先进的方法.
- 该方法成功地解决了模式偏差,在各种模式中创建了病变外观的一致表示.
结论:
- 为增强皮肤病诊断提出了一种多模式特征学习策略.
- 与其他最先进的方法相比,该方法显示出更高的性能.
- 这种技术有可能显著提高皮肤病变的诊断精度.
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